NeuralNetwork.cpp

/**
 * DESCRIPTION OF THE FILE
 *
 * @author Michal Kravčenko
 * @date 13.6.18 - 
 */

#include <iostream>

#include "../message.h"
#include "NeuralNetwork.h"
#include "NeuralNetworkSerialization.h"

namespace lib4neuro {
    NeuralNetwork::NeuralNetwork() {
        this->neurons = new std::vector<Neuron *>(0);
        this->neuron_biases = new std::vector<double>(0);
        this->neuron_potentials = new std::vector<double>(0);
        this->neuron_bias_indices = new std::vector<int>(0);

        this->connection_weights = new std::vector<double>(0);
        this->connection_list = new std::vector<ConnectionFunctionGeneral *>(0);
        this->inward_adjacency = new std::vector<std::vector<std::pair<size_t, size_t>> *>(0);
        this->outward_adjacency = new std::vector<std::vector<std::pair<size_t, size_t>> *>(0);

        this->neuron_layers_feedforward = new std::vector<std::vector<size_t> *>(0);
        this->neuron_layers_feedbackward = new std::vector<std::vector<size_t> *>(0);

        this->input_neuron_indices = new std::vector<size_t>(0);
        this->output_neuron_indices = new std::vector<size_t>(0);

        this->delete_weights = true;
        this->delete_biases = true;
        this->layers_analyzed = false;
    }

    NeuralNetwork::NeuralNetwork(std::string filepath) {
        std::ifstream ifs(filepath);
        boost::archive::text_iarchive ia(ifs);
        ia >> *this;
        ifs.close();
    }

    NeuralNetwork::~NeuralNetwork() {

        if (this->neurons) {
            for (auto n: *(this->neurons)) {
                delete n;
                n = nullptr;
            }
            delete this->neurons;
            this->neurons = nullptr;
        }

        if (this->neuron_potentials) {
            delete this->neuron_potentials;
            this->neuron_potentials = nullptr;
        }

        if (this->neuron_bias_indices) {
            delete this->neuron_bias_indices;
            this->neuron_bias_indices = nullptr;
        }

        if (this->output_neuron_indices) {
            delete this->output_neuron_indices;
            this->output_neuron_indices = nullptr;
        }

        if (this->input_neuron_indices) {
            delete this->input_neuron_indices;
            this->input_neuron_indices = nullptr;
        }

        if (this->connection_weights && this->delete_weights) {
            delete this->connection_weights;
            this->connection_weights = nullptr;
        }

        if (this->neuron_biases && this->delete_biases) {
            delete this->neuron_biases;
            this->neuron_biases = nullptr;
        }

        if (this->connection_list) {

            if (this->delete_weights) {
                for (auto c: *this->connection_list) {
                    delete c;
                    c = nullptr;
                }
            }
            delete this->connection_list;
            this->connection_list = nullptr;
        }

        if (this->inward_adjacency) {
            for (auto e: *this->inward_adjacency) {
                if (e) {
                    delete e;
                    e = nullptr;
                }
            }
            delete this->inward_adjacency;
            this->inward_adjacency = nullptr;
        }

        if (this->outward_adjacency) {
            for (auto e: *this->outward_adjacency) {
                if (e) {
                    delete e;
                    e = nullptr;
                }
            }
            delete this->outward_adjacency;
            this->outward_adjacency = nullptr;
        }

        if (this->neuron_layers_feedforward) {
            for (auto e: *this->neuron_layers_feedforward) {
                delete e;
                e = nullptr;
            }
            delete this->neuron_layers_feedforward;
            this->neuron_layers_feedforward = nullptr;
        }

        if (this->neuron_layers_feedbackward) {
            for (auto e: *this->neuron_layers_feedbackward) {
                delete e;
                e = nullptr;
            }
            delete this->neuron_layers_feedbackward;
            this->neuron_layers_feedbackward = nullptr;
        }
    }

    NeuralNetwork *
    NeuralNetwork::get_subnet(std::vector<size_t> &input_neuron_indices, std::vector<size_t> &output_neuron_indices) {
        NeuralNetwork *output_net = nullptr;
// TODO rework due to the changed structure of the class
//    Neuron * active_neuron, * target_neuron;
//
//    size_t n = this->neurons->size();
//    bool *part_of_subnetwork = new bool[n];
//    std::fill(part_of_subnetwork, part_of_subnetwork + n, false);
//
//    bool *is_reachable_from_source = new bool[n];
//    bool *is_reachable_from_destination = new bool[n];
//    std::fill(is_reachable_from_source, is_reachable_from_source + n, false);
//    std::fill(is_reachable_from_destination, is_reachable_from_destination + n, false);
//
//    bool *visited_neurons = new bool[n];
//    std::fill(visited_neurons, visited_neurons + n, false);
//
//    size_t active_set_size[2];
//    active_set_size[0] = 0;
//    active_set_size[1] = 0;
//    size_t * active_neuron_set = new size_t[2 * n];
//    size_t idx1 = 0, idx2 = 1;
//
//    /* MAPPING BETWEEN NEURONS AND THEIR INDICES */
//    size_t idx = 0, idx_target;
//    for(Neuron *v: *this->neurons){
//        v->set_idx( idx );
//        idx++;
//    }
//
//    /* INITIAL STATE FOR THE FORWARD PASS */
//    for(size_t i: input_neuron_indices ){
//
//        if( i < 0 || i >= n){
//            //invalid index
//            continue;
//        }
//        active_neuron_set[idx1 * n + active_set_size[idx1]] = i;
//        active_set_size[idx1]++;
//
//        visited_neurons[i] = true;
//    }
//
//    /* FORWARD PASS */
//    while(active_set_size[idx1] > 0){
//
//        //we iterate through the active neurons and propagate the signal
//        for(int i = 0; i < active_set_size[idx1]; ++i){
//            idx = active_neuron_set[i];
//
//            is_reachable_from_source[ idx ] = true;
//
//            active_neuron = this->neurons->at( idx );
//
//            for(Connection* connection: *(active_neuron->get_connections_out())){
//
//                target_neuron = connection->get_neuron_out( );
//                idx_target = target_neuron->get_idx( );
//
//                if( visited_neurons[idx_target] ){
//                    //this neuron was already analyzed
//                    continue;
//                }
//
//                visited_neurons[idx_target] = true;
//                active_neuron_set[active_set_size[idx2] + n * idx2] = idx_target;
//                active_set_size[idx2]++;
//            }
//        }
//        idx1 = idx2;
//        idx2 = (idx1 + 1) % 2;
//        active_set_size[idx2] = 0;
//    }
//
//
//    /* INITIAL STATE FOR THE FORWARD PASS */
//    active_set_size[0] = active_set_size[1] = 0;
//    std::fill(visited_neurons, visited_neurons + n, false);
//
//    for(size_t i: output_neuron_indices ){
//
//        if( i < 0 || i >= n){
//            //invalid index
//            continue;
//        }
//        active_neuron_set[idx1 * n + active_set_size[idx1]] = i;
//        active_set_size[idx1]++;
//
//        visited_neurons[i] = true;
//    }
//
//    /* BACKWARD PASS */
//    size_t n_new_neurons = 0;
//    while(active_set_size[idx1] > 0){
//
//        //we iterate through the active neurons and propagate the signal
//        for(int i = 0; i < active_set_size[idx1]; ++i){
//            idx = active_neuron_set[i];
//
//            is_reachable_from_destination[ idx ] = true;
//
//            active_neuron = this->neurons->at( idx );
//
//            if(is_reachable_from_source[ idx ]){
//                n_new_neurons++;
//            }
//
//            for(Connection* connection: *(active_neuron->get_connections_in())){
//
//                target_neuron = connection->get_neuron_in( );
//                idx_target = target_neuron->get_idx( );
//
//                if( visited_neurons[idx_target] ){
//                    //this neuron was already analyzed
//                    continue;
//                }
//
//                visited_neurons[idx_target] = true;
//                active_neuron_set[active_set_size[idx2] + n * idx2] = idx_target;
//                active_set_size[idx2]++;
//            }
//        }
//        idx1 = idx2;
//        idx2 = (idx1 + 1) % 2;
//        active_set_size[idx2] = 0;
//    }
//
//    /* FOR CONSISTENCY REASONS */
//    for(size_t in: input_neuron_indices){
//        if( !is_reachable_from_destination[in] ){
//            n_new_neurons++;
//        }
//        is_reachable_from_destination[in] = true;
//    }
//    /* FOR CONSISTENCY REASONS */
//    for(size_t in: output_neuron_indices){
//        if( !is_reachable_from_source[in] ){
//            n_new_neurons++;
//        }
//        is_reachable_from_source[in] = true;
//    }
//
//    /* WE FORM THE SET OF NEURONS IN THE OUTPUT NETWORK  */
//    if(n_new_neurons > 0){
////        printf("Number of new neurons: %d\n", n_new_neurons);
//        output_net = new NeuralNetwork();
//        output_net->set_weight_array( this->connection_weights );
//
//        std::vector<size_t > local_inputs(0), local_outputs(0);
//        local_inputs.reserve(input_neuron_indices.size());
//        local_outputs.reserve(output_neuron_indices.size());
//
//        std::vector<Neuron*> local_n_arr(0);
//        local_n_arr.reserve( n_new_neurons );
//
//        std::vector<Neuron*> local_local_n_arr(0);
//        local_local_n_arr.reserve( n_new_neurons );
//
//        int * neuron_local_mapping = new int[ n ];
//        std::fill(neuron_local_mapping, neuron_local_mapping + n, -1);
//        idx = 0;
//        for(size_t i = 0; i < n; ++i){
//            if(is_reachable_from_source[i] && is_reachable_from_destination[i]){
//                neuron_local_mapping[i] = (int)idx;
//                idx++;
//
//                Neuron *new_neuron = this->neurons->at(i)->get_copy( );
//
//                output_net->add_neuron( new_neuron );
//                local_local_n_arr.push_back( new_neuron );
//                local_n_arr.push_back( this->neurons->at(i) );
//            }
//        }
//        for(size_t in: input_neuron_indices){
//            local_inputs.push_back(neuron_local_mapping[in]);
//        }
//        for(size_t in: output_neuron_indices){
//            local_outputs.push_back(neuron_local_mapping[in]);
//        }
//
////        printf("%d\n", local_n_arr.size());
////        printf("inputs: %d, outputs: %d\n", local_inputs.size(), local_outputs.size());
//        int local_idx_1, local_idx_2;
//        for(Neuron* source_neuron: local_n_arr){
//            //we also add the relevant edges
//            local_idx_1 = neuron_local_mapping[source_neuron->get_idx()];
//
//            for(Connection* connection: *(source_neuron->get_connections_out( ))){
//                target_neuron = connection->get_neuron_out();
//
//                local_idx_2 = neuron_local_mapping[target_neuron->get_idx()];
//                if(local_idx_2 >= 0){
//                    //this edge is part of the subnetwork
//                    Connection* new_connection = connection->get_copy( local_local_n_arr[local_idx_1], local_local_n_arr[local_idx_2] );
//
//                    local_local_n_arr[local_idx_1]->add_connection_out(new_connection);
//                    local_local_n_arr[local_idx_2]->add_connection_in(new_connection);
//
////                    printf("adding a connection between neurons %d, %d\n", local_idx_1, local_idx_2);
//                }
//
//            }
//
//        }
//        output_net->specify_input_neurons(local_inputs);
//        output_net->specify_output_neurons(local_outputs);
//
//
//        delete [] neuron_local_mapping;
//    }
//
//    delete [] is_reachable_from_source;
//    delete [] is_reachable_from_destination;
//    delete [] part_of_subnetwork;
//    delete [] visited_neurons;
//    delete [] active_neuron_set;
//
//
        return output_net;
    }

    size_t NeuralNetwork::add_neuron(Neuron *n, BIAS_TYPE bt, size_t bias_idx) {

        if (bt == BIAS_TYPE::NO_BIAS) {
            this->neuron_bias_indices->push_back(-1);
        } else if (bt == BIAS_TYPE::NEXT_BIAS) {
            this->neuron_bias_indices->push_back((int) this->neuron_biases->size());
            this->neuron_biases->resize(this->neuron_biases->size() + 1);
        } else if (bt == BIAS_TYPE::EXISTING_BIAS) {
            if (bias_idx >= this->neuron_biases->size()) {
                std::cerr << "The supplied bias index is too large!\n" << std::endl;
            }
            this->neuron_bias_indices->push_back((int) bias_idx);
        }

        this->outward_adjacency->push_back(new std::vector<std::pair<size_t, size_t>>(0));
        this->inward_adjacency->push_back(new std::vector<std::pair<size_t, size_t>>(0));

        this->neurons->push_back(n);

        this->layers_analyzed = false;
        return this->neurons->size() - 1;
    }

    size_t
    NeuralNetwork::add_connection_simple(size_t n1_idx, size_t n2_idx, SIMPLE_CONNECTION_TYPE sct, size_t weight_idx) {

        ConnectionFunctionIdentity *con_weight_u1u2;
        if (sct == SIMPLE_CONNECTION_TYPE::UNITARY_WEIGHT) {
            con_weight_u1u2 = new ConnectionFunctionIdentity();
        } else {
            if (sct == SIMPLE_CONNECTION_TYPE::NEXT_WEIGHT) {
                weight_idx = this->connection_weights->size();
                this->connection_weights->resize(weight_idx + 1);
            } else if (sct == SIMPLE_CONNECTION_TYPE::EXISTING_WEIGHT) {
                if (weight_idx >= this->connection_weights->size()) {
                    std::cerr << "The supplied connection weight index is too large!\n" << std::endl;
                }
            }

            con_weight_u1u2 = new ConnectionFunctionIdentity(weight_idx);
        }

        size_t conn_idx = this->add_new_connection_to_list(con_weight_u1u2);

        this->add_outward_connection(n1_idx, n2_idx, conn_idx);
        this->add_inward_connection(n2_idx, n1_idx, conn_idx);

        this->layers_analyzed = false;

        return this->connection_list->size() - 1;
    }

    void NeuralNetwork::add_existing_connection(size_t n1_idx, size_t n2_idx, size_t connection_idx,
                                                NeuralNetwork &parent_network) {

        size_t conn_idx = this->add_new_connection_to_list(parent_network.connection_list->at(connection_idx));

        this->add_outward_connection(n1_idx, n2_idx, conn_idx);
        this->add_inward_connection(n2_idx, n1_idx, conn_idx);

        this->layers_analyzed = false;
    }

    void NeuralNetwork::copy_parameter_space(std::vector<double> *parameters) {
        if (parameters != nullptr) {
            for (unsigned int i = 0; i < this->connection_weights->size(); ++i) {
                (*this->connection_weights)[i] = (*parameters)[i];
            }

            for (unsigned int i = 0; i < this->neuron_biases->size(); ++i) {
                (*this->neuron_biases)[i] = (*parameters)[i + this->connection_weights->size()];
            }
        }
    }

    void NeuralNetwork::set_parameter_space_pointers(NeuralNetwork &parent_network) {

        if (this->connection_weights) {
            delete connection_weights;
        }

        if (this->neuron_biases) {
            delete this->neuron_biases;
        }

        this->connection_weights = parent_network.connection_weights;
        this->neuron_biases = parent_network.neuron_biases;

        this->delete_biases = false;
        this->delete_weights = false;
    }

    void NeuralNetwork::eval_single(std::vector<double> &input, std::vector<double> &output,
                                    std::vector<double> *custom_weights_and_biases) {
        if ((this->input_neuron_indices->size() * this->output_neuron_indices->size()) <= 0) {
            std::cerr << "Input and output neurons have not been specified\n" << std::endl;
            exit(-1);
        }


        if (this->input_neuron_indices->size() != input.size()) {
            std::cerr << "Error, input size != Network input size\n" << std::endl;
            exit(-1);
        }

        if (this->output_neuron_indices->size() != output.size()) {
            std::cerr << "Error, output size != Network output size\n" << std::endl;
            exit(-1);
        }
        double potential, bias;
        int bias_idx;

        this->copy_parameter_space(custom_weights_and_biases);

        this->analyze_layer_structure();

        /* reset of the output and the neuron potentials */
        std::fill(output.begin(), output.end(), 0.0);
        std::fill(this->neuron_potentials->begin(), this->neuron_potentials->end(), 0.0);

        /* set the potentials of the input neurons */
        for (size_t i = 0; i < this->input_neuron_indices->size(); ++i) {
            this->neuron_potentials->at(this->input_neuron_indices->at(i)) = input[i];
        }

        /* we iterate through all the feed-forward layers and transfer the signals */
        for (auto layer: *this->neuron_layers_feedforward) {
            /* we iterate through all neurons in this layer and propagate the signal to the neighboring neurons */

            for (auto si: *layer) {
                bias = 0.0;
                bias_idx = this->neuron_bias_indices->at(si);
                if (bias_idx >= 0) {
                    bias = this->neuron_biases->at(bias_idx);
                }
                potential = this->neurons->at(si)->activate(this->neuron_potentials->at(si), bias);

                for (auto c: *this->outward_adjacency->at(si)) {
                    size_t ti = c.first;
                    size_t ci = c.second;

                    this->neuron_potentials->at(ti) +=
                            this->connection_list->at(ci)->eval(*this->connection_weights) * potential;
                }
            }
        }

        unsigned int i = 0;
        for (auto oi: *this->output_neuron_indices) {
            bias = 0.0;
            bias_idx = this->neuron_bias_indices->at(oi);
            if (bias_idx >= 0) {
                bias = this->neuron_biases->at(bias_idx);
            }
            output[i] = this->neurons->at(oi)->activate(this->neuron_potentials->at(oi), bias);
            ++i;
        }
    }

    void NeuralNetwork::randomize_weights() {

        boost::random::mt19937 gen;

        // Init weight guess ("optimal" for logistic activation functions)
        double r = 4 * sqrt(6. / (this->connection_weights->size()));

        boost::random::uniform_real_distribution<> dist(-r, r);

        for (size_t i = 0; i < this->connection_weights->size(); i++) {
            this->connection_weights->at(i) = dist(gen);
        }
    }

    void NeuralNetwork::randomize_biases() {

        boost::random::mt19937 gen;

        // Init weight guess ("optimal" for logistic activation functions)
        boost::random::uniform_real_distribution<> dist(-1, 1);
        for (size_t i = 0; i < this->neuron_biases->size(); i++) {
            this->neuron_biases->at(i) = dist(gen);
        }
    }

    size_t NeuralNetwork::get_n_inputs() {
        return this->input_neuron_indices->size();
    }

    size_t NeuralNetwork::get_n_outputs() {
        return this->output_neuron_indices->size();
    }

    size_t NeuralNetwork::get_n_weights() {
        return this->connection_weights->size();
    }

    size_t NeuralNetwork::get_n_biases() {
        return this->neuron_biases->size();
    }

    int NeuralNetwork::get_neuron_bias_index(size_t neuron_idx) {
        return this->neuron_bias_indices->at(neuron_idx);
    }

    size_t NeuralNetwork::get_n_neurons() {
        return this->neurons->size();
    }

    void NeuralNetwork::specify_input_neurons(std::vector<size_t> &input_neurons_indices) {
        if (!this->input_neuron_indices) {
            this->input_neuron_indices = new std::vector<size_t>(input_neurons_indices);
        } else {
            delete this->input_neuron_indices;
            this->input_neuron_indices = new std::vector<size_t>(input_neurons_indices);
        }
    }

    void NeuralNetwork::specify_output_neurons(std::vector<size_t> &output_neurons_indices) {
        if (!this->output_neuron_indices) {
            this->output_neuron_indices = new std::vector<size_t>(output_neurons_indices);
        } else {
            delete this->output_neuron_indices;
            this->output_neuron_indices = new std::vector<size_t>(output_neurons_indices);
        }
    }

    void NeuralNetwork::print_weights() {
        std::cout << "Connection weights: ";
        if (this->connection_weights) {
            for (size_t i = 0; i < this->connection_weights->size() - 1; ++i) {
                std::cout << this->connection_weights->at(i) << " ";
            }
            std::cout << this->connection_weights->at(this->connection_weights->size() - 1);
        }

        std::cout << std::endl;
    }

    void NeuralNetwork::print_stats() {
        std::cout << "Number of neurons: " << this->neurons->size() << std::endl
                  << "Number of connections: " << this->connection_list->size() << std::endl
                  << "Number of active weights: " << this->connection_weights->size() << std::endl
                  << "Number of active biases: " << this->neuron_biases->size() << std::endl;
    }

    std::vector<double> *NeuralNetwork::get_parameter_ptr_biases() {
        return this->neuron_biases;
    }

    std::vector<double> *NeuralNetwork::get_parameter_ptr_weights() {
        return this->connection_weights;
    }

    size_t NeuralNetwork::add_new_connection_to_list(ConnectionFunctionGeneral *con) {
        this->connection_list->push_back(con);
        return this->connection_list->size() - 1;
    }

    void NeuralNetwork::add_inward_connection(size_t s, size_t t, size_t con_idx) {
        if (!this->inward_adjacency->at(s)) {
            this->inward_adjacency->at(s) = new std::vector<std::pair<size_t, size_t>>(0);
        }
        this->inward_adjacency->at(s)->push_back(std::pair<size_t, size_t>(t, con_idx));
    }

    void NeuralNetwork::add_outward_connection(size_t s, size_t t, size_t con_idx) {
        if (!this->outward_adjacency->at(s)) {
            this->outward_adjacency->at(s) = new std::vector<std::pair<size_t, size_t>>(0);
        }
        this->outward_adjacency->at(s)->push_back(std::pair<size_t, size_t>(t, con_idx));
    }

    void NeuralNetwork::analyze_layer_structure() {

        if (this->layers_analyzed) {
            //nothing to do
            return;
        }

        /* buffer preparation */
        this->neuron_potentials->resize(this->get_n_neurons());

        /* space allocation */
        if (this->neuron_layers_feedforward) {
            for (auto e: *this->neuron_layers_feedforward) {
                delete e;
                e = nullptr;
            }
            delete this->neuron_layers_feedforward;
            this->neuron_layers_feedforward = nullptr;
        }

        if (this->neuron_layers_feedbackward) {
            for (auto e: *this->neuron_layers_feedbackward) {
                delete e;
                e = nullptr;
            }
            delete this->neuron_layers_feedbackward;
            this->neuron_layers_feedbackward = nullptr;
        }

        this->neuron_layers_feedforward = new std::vector<std::vector<size_t> *>(0);
        this->neuron_layers_feedbackward = new std::vector<std::vector<size_t> *>(0);


        auto n = this->neurons->size();

        /* helpful counters */
        std::vector<size_t> inward_saturation(n);
        std::vector<size_t> outward_saturation(n);
        std::fill(inward_saturation.begin(), inward_saturation.end(), 0);
        std::fill(outward_saturation.begin(), outward_saturation.end(), 0);
        for (unsigned int i = 0; i < n; ++i) {
            if (this->inward_adjacency->at(i)) {
                inward_saturation[i] = this->inward_adjacency->at(i)->size();
            }

            if (this->outward_adjacency->at(i)) {
                outward_saturation[i] = this->outward_adjacency->at(i)->size();
            }
        }


        std::vector<size_t> active_eval_set(2 * n);
        size_t active_set_size[2];

        /* feedforward analysis */
        active_set_size[0] = 0;
        active_set_size[1] = 0;

        size_t idx1 = 0, idx2 = 1;

        active_set_size[0] = this->get_n_inputs();
        size_t i = 0;
        for (i = 0; i < this->get_n_inputs(); ++i) {
            active_eval_set[i] = this->input_neuron_indices->at(i);
        }

        size_t active_ni;
        while (active_set_size[idx1] > 0) {

            /* we add the current active set as the new outward layer */
            std::vector<size_t> *new_feedforward_layer = new std::vector<size_t>(active_set_size[idx1]);
            this->neuron_layers_feedforward->push_back(new_feedforward_layer);

            //we iterate through the active neurons and propagate the signal
            for (i = 0; i < active_set_size[idx1]; ++i) {
                active_ni = active_eval_set[i + n * idx1];
                new_feedforward_layer->at(i) = active_ni;

                if (!this->outward_adjacency->at(active_ni)) {
                    continue;
                }

                for (auto ni: *(this->outward_adjacency->at(active_ni))) {
                    inward_saturation[ni.first]--;

                    if (inward_saturation[ni.first] == 0) {
                        active_eval_set[active_set_size[idx2] + n * idx2] = ni.first;
                        active_set_size[idx2]++;
                    }
                }
            }

            idx1 = idx2;
            idx2 = (idx1 + 1) % 2;

            active_set_size[idx2] = 0;
        }


        /* feed backward analysis */
        active_set_size[0] = 0;
        active_set_size[1] = 0;

        idx1 = 0;
        idx2 = 1;

        active_set_size[0] = this->get_n_outputs();
        for (i = 0; i < this->get_n_outputs(); ++i) {
            active_eval_set[i] = this->output_neuron_indices->at(i);
        }

        while (active_set_size[idx1] > 0) {

            /* we add the current active set as the new outward layer */
            std::vector<size_t> *new_feedbackward_layer = new std::vector<size_t>(active_set_size[idx1]);
            this->neuron_layers_feedbackward->push_back(new_feedbackward_layer);

            //we iterate through the active neurons and propagate the signal backward
            for (i = 0; i < active_set_size[idx1]; ++i) {
                active_ni = active_eval_set[i + n * idx1];
                new_feedbackward_layer->at(i) = active_ni;

                if (!this->inward_adjacency->at(active_ni)) {
                    continue;
                }

                for (auto ni: *(this->inward_adjacency->at(active_ni))) {
                    outward_saturation[ni.first]--;

                    if (outward_saturation[ni.first] == 0) {
                        active_eval_set[active_set_size[idx2] + n * idx2] = ni.first;
                        active_set_size[idx2]++;
                    }
                }
            }

            idx1 = idx2;
            idx2 = (idx1 + 1) % 2;

            active_set_size[idx2] = 0;
        }

        this->layers_analyzed = true;
    }

    void NeuralNetwork::save_text(std::string filepath) {
        std::ofstream ofs(filepath);
        {
            boost::archive::text_oarchive oa(ofs);
            oa << *this;
            ofs.close();
        }
    }

}