layersNeuronsCount.add(0, numberOfInputs);
layersNeuronsCount.add(numberOfOutputs);
NeuralNetwork neuralNetwork = new MultiLayerPerceptron(layersNeuronsCount, transferFunction);
neuralNetwork.setLabel(neuralNetworkName);
neuralNetwork.setLearningRule(new MomentumBackpropagation());
<BUG>Neuron[] outputNeurons = neuralNetwork.getOutputNeurons();

for(int i=0; i<outputNeurons.length; i++) {

outputNeurons[i].setLabel(classes[i]);
</BUG>
}

layersNeuronsCount.add(0, numberOfInputs);
layersNeuronsCount.add(numberOfOutputs);
NeuralNetwork neuralNetwork = new MultiLayerPerceptron(layersNeuronsCount, transferFunction);
neuralNetwork.setLabel(neuralNetworkName);
neuralNetwork.setLearningRule(new MomentumBackpropagation());
List<Neuron> outputNeurons = neuralNetwork.getOutputNeurons();
for(int i=0; i<outputNeurons.size(); i++) {
outputNeurons.get(i).setLabel(classes[i]);
}
