/**
   * 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();
  }