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Michal Kravcenko
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/**
* DESCRIPTION OF THE FILE
*
* @author Michal Kravčenko
* @date 13.6.18 -
*/
#include "NeuralNetwork.h"
Michal Kravcenko
committed
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NeuralNetwork::NeuralNetwork() {
this->neurons = new std::vector<Neuron*>(0);
}
NeuralNetwork::~NeuralNetwork() {
if(this->neurons){
delete this->neurons;
this->neurons = nullptr;
}
if(this->output_neurons){
delete this->output_neurons;
this->output_neurons = nullptr;
}
if(this->input_neurons){
delete this->input_neurons;
this->input_neurons = nullptr;
}
if(this->active_eval_set){
delete this->active_eval_set;
this->active_eval_set = nullptr;
}
}
void NeuralNetwork::add_neuron(Neuron *n) {
this->neurons->push_back(n);
this->in_out_determined = false;
}
void NeuralNetwork::determine_inputs_outputs() {
if(this->output_neurons){
delete this->output_neurons;
}
if(this->input_neurons){
delete this->input_neurons;
}
this->output_neurons = new std::vector<Neuron*>(0);
this->input_neurons = new std::vector<Neuron*>(0);
if(this->active_eval_set == nullptr){
this->active_eval_set = new std::vector<Neuron*>(this->neurons->size() * 2);
}
else{
this->active_eval_set->resize(this->neurons->size() * 2);
}
for(Neuron* neuron: *this->neurons){
if(neuron->get_connections_out()->empty()){
//this neuron has no outgoing connections, it is the output neuron
this->output_neurons->push_back(neuron);
}
else if(neuron->get_connections_in()->empty()){
//this neuron has no incoming connections, it is the input neuron
this->input_neurons->push_back(neuron);
}
}
this->n_inputs = (int)this->input_neurons->size();
this->n_outputs = (int)this->output_neurons->size();
this->in_out_determined = true;
}
void NeuralNetwork::eval_single(std::vector<double> &input, std::vector<double> &output) {
if(!this->in_out_determined){
this->determine_inputs_outputs();
}
if(this->n_inputs != input.size()){
printf("Error, input size != Network input size\n");
return;
}
if(this->n_outputs != output.size()){
printf("Error, output size != Network output size\n");
return;
}
std::fill(output.begin(), output.end(), 0.0);
//reset of the potentials
for(Neuron* neuron: *this->neurons){
neuron->set_potential(0.0);
neuron->set_saturation_in(0);
neuron->set_saturation_out(0);
}
int active_set_size[2];
active_set_size[0] = 0;
active_set_size[1] = 0;
int idx1 = 0, idx2 = 1;
active_set_size[0] = this->n_inputs;
int i = 0;
auto n = this->neurons->size();
for(Neuron* neuron: *this->input_neurons){
this->active_eval_set->at(i) = neuron;
neuron->set_potential(input[i]);
++i;
}
Neuron* active_neuron;
Neuron* target_neuron;
while(active_set_size[idx1] > 0){
//we iterate through the active neurons and propagate the signal
for(i = 0; i < active_set_size[idx1]; ++i){
active_neuron = this->active_eval_set->at(i + n * idx1);
active_neuron->activate();
for(Connection* connection: *(active_neuron->get_connections_out())){
connection->pass_signal();
target_neuron = connection->get_neuron_out();
target_neuron->adjust_saturation_in(1);
if(target_neuron->is_saturated_in()){
this->active_eval_set->at(active_set_size[idx2] + n * idx2) = target_neuron;
active_set_size[idx2]++;
}
}
}
idx1 = idx2;
idx2 = (idx1 + 1) % 2;
active_set_size[idx2] = 0;
}
i = 0;
for(Neuron* neuron: *this->output_neurons){
output[i] = neuron->get_state();
++i;
}
}