this.pi.repaint();
}
}
private void performTrain() {
InputNEAT dialog = new InputNEAT();
if (dialog.process()) {
ProjectEGFile popFile = dialog.getPopulation();
NEATPopulation pop = (NEATPopulation) popFile.getObject();
pop.setInputCount(2);
pop.setOutputCount(1);
MLDataSet training = dialog.getTrainingSet();
if (dialog.getLoadToMemory().getValue()) {
training = ((BufferedNeuralDataSet) training).loadToMemory();
}
CalculateScore score = new TrainingSetScore(training);
NEATTraining train = new NEATTraining(score, pop);
BasicTrainingProgress tab = new BasicTrainingProgress(train,
popFile, train.getTraining());
tab.setMaxError(dialog.getMaxError().getValue() / 100);
EncogWorkBench.getInstance().getMainWindow().getTabManager().openTab(tab);
}
}