/**
* Neural networks with only one type of activation function offer certain optimization options.
* This method determines if only a single activation function is used.
*
* @return The number of the single activation function, or -1 if there are no activation
* functions or more than one type of activation function.
*/
public Class<?> hasSameActivationFunction() {
final List<Class<?>> map = new ArrayList<Class<?>>();
for (final ActivationFunction activation : this.activationFunctions) {
if (!map.contains(activation.getClass())) {
map.add(activation.getClass());
}
}
if (map.size() != 1) {
return null;
} else {
return map.get(0);
}
}
/** {@inheritDoc} */
@Override
public void save(final OutputStream os, final Object obj) {
final EncogWriteHelper out = new EncogWriteHelper(os);
final BasicNetwork net = (BasicNetwork) obj;
final FlatNetwork flat = net.getStructure().getFlat();
out.addSection("BASIC");
out.addSubSection("PARAMS");
out.addProperties(net.getProperties());
out.addSubSection("NETWORK");
out.writeProperty(BasicNetwork.TAG_BEGIN_TRAINING, flat.getBeginTraining());
out.writeProperty(BasicNetwork.TAG_CONNECTION_LIMIT, flat.getConnectionLimit());
out.writeProperty(BasicNetwork.TAG_CONTEXT_TARGET_OFFSET, flat.getContextTargetOffset());
out.writeProperty(BasicNetwork.TAG_CONTEXT_TARGET_SIZE, flat.getContextTargetSize());
out.writeProperty(BasicNetwork.TAG_END_TRAINING, flat.getEndTraining());
out.writeProperty(BasicNetwork.TAG_HAS_CONTEXT, flat.getHasContext());
out.writeProperty(PersistConst.INPUT_COUNT, flat.getInputCount());
out.writeProperty(BasicNetwork.TAG_LAYER_COUNTS, flat.getLayerCounts());
out.writeProperty(BasicNetwork.TAG_LAYER_FEED_COUNTS, flat.getLayerFeedCounts());
out.writeProperty(BasicNetwork.TAG_LAYER_CONTEXT_COUNT, flat.getLayerContextCount());
out.writeProperty(BasicNetwork.TAG_LAYER_INDEX, flat.getLayerIndex());
out.writeProperty(PersistConst.OUTPUT, flat.getLayerOutput());
out.writeProperty(PersistConst.OUTPUT_COUNT, flat.getOutputCount());
out.writeProperty(BasicNetwork.TAG_WEIGHT_INDEX, flat.getWeightIndex());
out.writeProperty(PersistConst.WEIGHTS, flat.getWeights());
out.writeProperty(BasicNetwork.TAG_BIAS_ACTIVATION, flat.getBiasActivation());
out.addSubSection("ACTIVATION");
for (final ActivationFunction af : flat.getActivationFunctions()) {
String sn = af.getClass().getSimpleName();
// if this is an Encog class then only add the simple name, so it works with C#
if (sn.startsWith("org.encog.")) {
out.addColumn(sn);
} else {
out.addColumn(af.getClass().getName());
}
for (int i = 0; i < af.getParams().length; i++) {
out.addColumn(af.getParams()[i]);
}
out.writeLine();
}
out.flush();
}
/** {@inheritDoc} */
@Override
public void save(final OutputStream os, final Object obj) {
final EncogWriteHelper out = new EncogWriteHelper(os);
final RBFNetwork net = (RBFNetwork) obj;
final FlatNetworkRBF flat = (FlatNetworkRBF) net.getFlat();
out.addSection("RBF-NETWORK");
out.addSubSection("PARAMS");
out.addProperties(net.getProperties());
out.addSubSection("NETWORK");
out.writeProperty(BasicNetwork.TAG_BEGIN_TRAINING, flat.getBeginTraining());
out.writeProperty(BasicNetwork.TAG_CONNECTION_LIMIT, flat.getConnectionLimit());
out.writeProperty(BasicNetwork.TAG_CONTEXT_TARGET_OFFSET, flat.getContextTargetOffset());
out.writeProperty(BasicNetwork.TAG_CONTEXT_TARGET_SIZE, flat.getContextTargetSize());
out.writeProperty(BasicNetwork.TAG_END_TRAINING, flat.getEndTraining());
out.writeProperty(BasicNetwork.TAG_HAS_CONTEXT, flat.getHasContext());
out.writeProperty(PersistConst.INPUT_COUNT, flat.getInputCount());
out.writeProperty(BasicNetwork.TAG_LAYER_COUNTS, flat.getLayerCounts());
out.writeProperty(BasicNetwork.TAG_LAYER_FEED_COUNTS, flat.getLayerFeedCounts());
out.writeProperty(BasicNetwork.TAG_LAYER_CONTEXT_COUNT, flat.getLayerContextCount());
out.writeProperty(BasicNetwork.TAG_LAYER_INDEX, flat.getLayerIndex());
out.writeProperty(PersistConst.OUTPUT, flat.getLayerOutput());
out.writeProperty(PersistConst.OUTPUT_COUNT, flat.getOutputCount());
out.writeProperty(BasicNetwork.TAG_WEIGHT_INDEX, flat.getWeightIndex());
out.writeProperty(PersistConst.WEIGHTS, flat.getWeights());
out.writeProperty(BasicNetwork.TAG_BIAS_ACTIVATION, flat.getBiasActivation());
out.addSubSection("ACTIVATION");
for (final ActivationFunction af : flat.getActivationFunctions()) {
out.addColumn(af.getClass().getSimpleName());
for (int i = 0; i < af.getParams().length; i++) {
out.addColumn(af.getParams()[i]);
}
out.writeLine();
}
out.addSubSection("RBF");
for (final RadialBasisFunction rbf : flat.getRBF()) {
out.addColumn(rbf.getClass().getSimpleName());
out.addColumn(rbf.getWidth());
out.addColumn(rbf.getPeak());
for (int i = 0; i < rbf.getCenters().length; i++) {
out.addColumn(rbf.getCenters()[i]);
}
out.writeLine();
}
out.flush();
}