void MainWindow::on_pushButton_assignTime_clicked()
{
int tsID = ui->tableWidget_timeStamps->getSelectedID();
int runID = ui->tableWidget_main->currentRow()+1;
RunData* run = competition()->getRun(runID);
TimeStamp* ts = competition()->getTimeStamp(tsID);
if(ts && run){
assignmentManager.tryToAssignTimeStampToRun(tsID, runID);
}
}
void MainWindow::on_pushButton_manualTrigger_clicked()
{
TimeStamp* ts = new TimeStamp(0, getTimeBaseTime(), TimeStamp::M);
competition()->addTimeStamp(ts);
assignmentManager.tryToAssignTimeStampToRun(ts->getID());
}
bool SOMClusteringSpace::start_algorithm() {
if ( initialize_algorithm() ) {
int counter = 0;
float global_error = MAX_FLOAT;
int number_of_patterns = pGraph->numberOfNodes();
Iterator<node>* node_iter = 0;
patterns = pGraph->getProperty<DoubleVectorProperty>( patterns_prop_name );
if (number_of_patterns > 0 && patterns != 0) {
while (counter < lambda && global_error > epsilon) {
node_iter = pGraph->getNodes();
while( node_iter->hasNext() )
{
node n = node_iter->next();
int winner_index = competition( n );
assignation[n.id] = winner_index;
}
delete node_iter;
counter++;
learning_rate /= 1.1;
}
}
return true;
}
return false;
}
unsigned int som::train(bool autostop) {
previous_weights = NULL;
for (unsigned int epouch = 1; epouch < (epouchs + 1); epouch++) {
/* Depression term of coupling */
local_radius = std::pow( ( params.init_radius * std::exp(-( (double) epouch / (double) epouchs)) ), 2);
learn_rate = params.init_learn_rate * std::exp(-( (double) epouch / (double) epouchs));
/* Feature SOM 0003: Clear statistics */
if (autostop == true) {
for (unsigned int i = 0; i < size; i++) {
(*awards)[i] = 0;
(*capture_objects)[i]->clear();
}
}
for (unsigned int i = 0; i < data->size(); i++) {
/* Step 1: Competition */
unsigned int index_winner = competition(&(*data)[i]);
/* Step 2: Adaptation */
adaptation(index_winner, &(*data)[i]);
/* Update statistics */
if ( (autostop == true) || (epouch == (epouchs - 1)) ) {
(*awards)[index_winner]++;
(*capture_objects)[index_winner]->push_back(i);
}
}
/* Feature SOM 0003: Check requirement of stopping */
if (autostop == true) {
if (previous_weights == NULL) {
previous_weights = new std::vector<std::vector<double> * >(size, NULL);
unsigned int dimensions = (*weights)[0]->size();
for (unsigned int i = 0; i < weights->size(); i++) {
(*previous_weights)[i] = new std::vector<double>(dimensions, 0.0);
std::copy((*weights)[i]->begin(), (*weights)[i]->end(), (*previous_weights)[i]->begin());
}
}
else {
double maximal_adaptation = calculate_maximal_adaptation();
if (maximal_adaptation < params.adaptation_threshold) {
return epouch;
}
for (unsigned int i = 0; i < weights->size(); i++) {
std::copy((*weights)[i]->begin(), (*weights)[i]->end(), (*previous_weights)[i]->begin());
}
}
}
}
return epouchs;
}
unsigned int som::simulate(const std::vector<double> * pattern) const {
return competition(pattern);
}
