/** Sets default input and output neurons for network (first layer as input, last as output) */
public static void setDefaultIO(NeuralNetwork nnet) {
ArrayList<Neuron> inputNeuronsList = new ArrayList<Neuron>();
Layer firstLayer = nnet.getLayerAt(0);
for (Neuron neuron : firstLayer.getNeurons()) {
if (!(neuron instanceof BiasNeuron)) { // dont set input to bias
// neurons
inputNeuronsList.add(neuron);
}
}
Neuron[] inputNeurons = new Neuron[inputNeuronsList.size()];
inputNeurons = inputNeuronsList.toArray(inputNeurons);
Neuron[] outputNeurons = ((Layer) nnet.getLayerAt(nnet.getLayersCount() - 1)).getNeurons();
nnet.setInputNeurons(inputNeurons);
nnet.setOutputNeurons(outputNeurons);
}
private void learnPattern(DataSetRow dataSetRow, int neighborhood) {
neuralNetwork.setInput(dataSetRow.getInput());
neuralNetwork.calculate();
Neuron winner = getClosest();
if (winner.getOutput() == 0) return; // ako je vec istrenirana jedna celija, izadji
Layer mapLayer = neuralNetwork.getLayerAt(1);
int winnerIdx = mapLayer.indexOf(winner);
adjustCellWeights(winner, 0);
int cellNum = mapLayer.getNeuronsCount();
for (int p = 0; p < cellNum; p++) {
if (p == winnerIdx) continue;
if (isNeighbor(winnerIdx, p, neighborhood)) {
Neuron cell = mapLayer.getNeuronAt(p);
adjustCellWeights(cell, 1);
} // if
} // for
}
