simulator.cpp

//
// Created by martin on 25.11.18.
//


#include <iostream>
#include <cstdio>
#include <fstream>
#include <vector>
#include <utility>
#include <algorithm>
#include <assert.h>

#include "4neuro.h"

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

    bool normalize_data = true;
    double prec = 1e-9;
    double prec_lm = 1e-15;
    int restart_interval = 500;
    int max_n_iters_gradient = 10000;
    int max_n_iters_gradient_lm = 10000;

    int max_n_iters_swarm = 20;
    int n_particles_swarm = 200;
    int batch_size = 0;
    int max_number_of_cycles = 5;
    try {
        /* PHASE 1 - TRAINING DATA LOADING, NETWORK ASSEMBLY AND PARTICLE SWARM OPTIMIZATION */
        l4n::CSVReader reader1("/home/fluffymoo/Dropbox/data_BACK_RH_1.csv", ";", true);  // File, separator, skip 1st line
        reader1.read();  // Read from the file

        /* PHASE 1 - NEURAL NETWORK SPECIFICATION */
        /* Create data set for both the first training of the neural network */
        /* Specify which columns are inputs or outputs */
        std::vector<unsigned int> inputs = { 0 };  // Possible multiple inputs, e.g. {0,3}, column indices starting from 0
        std::vector<unsigned int> outputs = { 2 };  // Possible multiple outputs, e.g. {1,2}
        l4n::DataSet ds1 = reader1.get_data_set(&inputs, &outputs);  // Creation of data-set for NN
        if(normalize_data){
            ds1.normalize();  // Normalization of data to prevent numerical problems
        }

        /* Numbers of neurons in layers (including input and output layers) */
        std::vector<unsigned int> neuron_numbers_in_layers = { 1, 6, 6, 1 };

        /* Fully connected feed-forward network with linear activation functions for input and output */
        /* layers and the specified activation fns for the hidden ones (each entry = layer)*/
        std::vector<l4n::NEURON_TYPE> hidden_type_v = { l4n::NEURON_TYPE::LOGISTIC, l4n::NEURON_TYPE::LOGISTIC, l4n::NEURON_TYPE::LOGISTIC, l4n::NEURON_TYPE::LOGISTIC, l4n::NEURON_TYPE::LOGISTIC }; // hidden_type_v = {l4n::NEURON_TYPE::LOGISTIC, l4n::NEURON_TYPE::LINEAR}
        l4n::FullyConnectedFFN nn1(&neuron_numbers_in_layers, &hidden_type_v);

        /* Error function */
        l4n::MSE mse1(&nn1, &ds1);  // First parameter - neural network, second parameter - data-set

        /* Particle Swarm method domain*/
        std::vector<double> domain_bounds(2 * (nn1.get_n_weights() + nn1.get_n_biases()));
        for (size_t i = 0; i < domain_bounds.size() / 2; ++i) {
            domain_bounds[2 * i] = -0.1;
            domain_bounds[2 * i + 1] = 0.1;
        }

        // Parameters of the Particle Swarm
        // 1) domain_bounds Bounds for every optimized parameter (p1_lower, p1_upper, p2_lower, p2_upper...)
        // 2) c1 Cognitive parameter
        // 3) c2 Social parameter
        // 4) w Inertia weight
        // 5) gamma Threshold value for particle velocity - all particles must posses the same or slower velocity for the algorithm to end
        // 6) epsilon Radius of the cluster area (Euclidean distance)
        // 7) delta Amount of particles, which has to be in the cluster for the algorithm to stop (0-1)
        // 8) n_particles Number of particles in the swarm
        // 9) iter_max Maximal number of iterations - optimization will stop after that, even if not converged
        l4n::ParticleSwarm ps(&domain_bounds,
                              1.711897,
                              1.711897,
                              0.711897,
                              0.5,
                              0.3,
                              0.7,
                              n_particles_swarm,
                              max_n_iters_swarm);

        // Parameters of the gradient descent
        // 1) Threshold for the successful ending of the optimization - deviation from minima
        // 2) Number of iterations to reset step size to tolerance/10.0
        // 3) Maximal number of iterations - optimization will stop after that, even if not converged
        l4n::GradientDescent gs_(prec, restart_interval, max_n_iters_gradient, batch_size);
        l4n::GradientDescentBB gs(prec, restart_interval, max_n_iters_gradient, batch_size);
        l4n::GradientDescentSingleItem gs_si(prec, 0, 5000);//TODO needs improvement
        l4n::LevenbergMarquardt leven(max_n_iters_gradient_lm, prec_lm);
        l4n::LearningSequence learning_sequence( 1e-6, max_number_of_cycles );

        learning_sequence.add_learning_method( &ps );
//        learning_sequence.add_learning_method( &gs );
        learning_sequence.add_learning_method( &leven );
//        learning_sequence.add_learning_method( &gs_ );
//        learning_sequence.add_learning_method( &gs_si );
//        learning_sequence.add_learning_method( &gs );

        /* Weight and bias randomization in the network accordingly to the uniform distribution */
        nn1.randomize_parameters();

        /* Complex Optimization */
        learning_sequence.optimize(mse1);  // Network training

        /* Save Neural network parameters to file */
        nn1.save_text("test_net_Gradient_Descent.4n");

        /* PHASE 4 - TESTING DATA */

//        /* Output file specification */
        std::string filename = "simulator_output.txt";
        std::ofstream output_file(filename);
        if (!output_file.is_open()) {
            throw std::runtime_error("File '" + filename + "' can't be opened!");
        }
//
//        /* Neural network loading */
        l4n::NeuralNetwork nn3("test_net_Gradient_Descent.4n");

        /* Check of the saved network - write to the file */
        output_file << std::endl << "The loaded network info:" << std::endl;
        nn3.write_stats(&output_file);
        nn3.write_weights(&output_file);
        nn3.write_biases(&output_file);
//
//        /* Evaluate network on an arbitrary data-set and save results into the file */
        l4n::CSVReader reader3("/home/fluffymoo/Dropbox/data_BACK_RH_1.csv", ";", true);  // File, separator, skip 1st line
        reader3.read();  // Read from the file
//
//        /* Create data set for both the testing of the neural network */
//        /* Specify which columns are inputs or outputs */
//
        l4n::DataSet ds3 = reader3.get_data_set(&inputs, &outputs);  // Creation of data-set for NN
        if(normalize_data){
            ds3.normalize();  // Normalization of data to prevent numerical problems
        }
//
//        output_file << std::endl << "Evaluating network on the dataset: " << std::endl;
//        ds3.store_data_text(&output_file);
//
        output_file << "Output and the error:" << std::endl;
//
//        /* Error function */
        l4n::MSE mse3(&nn3, &ds3);  // First parameter - neural network, second parameter - data-set

        mse3.eval_on_data_set(&ds3, &output_file, nullptr, normalize_data, true);

        /* Close the output file for writing */
        output_file.close();

        return 0;

    }
    catch (const std::exception& e) {
        std::cerr << e.what() << std::endl;
        exit(EXIT_FAILURE);
    }

}