Package org.encog.neural.flat

Examples of org.encog.neural.flat.FlatNetworkRBF

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   * {@inheritDoc}
   */
  @Override
  public final Object read(final InputStream is) {
    final RBFNetwork result = new RBFNetwork();
    final FlatNetworkRBF flat = (FlatNetworkRBF) result.getFlat();

    final EncogReadHelper in = new EncogReadHelper(is);
    EncogFileSection section;

    while ((section = in.readNextSection()) != null) {
      if (section.getSectionName().equals("RBF-NETWORK")
          && section.getSubSectionName().equals("PARAMS")) {
        final Map<String, String> params = section.parseParams();
        result.getProperties().putAll(params);
      }
      if (section.getSectionName().equals("RBF-NETWORK")
          && section.getSubSectionName().equals("NETWORK")) {
        final Map<String, String> params = section.parseParams();

        flat.setBeginTraining(EncogFileSection.parseInt(params,
            BasicNetwork.TAG_BEGIN_TRAINING));
        flat.setConnectionLimit(EncogFileSection.parseDouble(params,
            BasicNetwork.TAG_CONNECTION_LIMIT));
        flat.setContextTargetOffset(EncogFileSection.parseIntArray(
            params, BasicNetwork.TAG_CONTEXT_TARGET_OFFSET));
        flat.setContextTargetSize(EncogFileSection.parseIntArray(
            params, BasicNetwork.TAG_CONTEXT_TARGET_SIZE));
        flat.setEndTraining(EncogFileSection.parseInt(params,
            BasicNetwork.TAG_END_TRAINING));
        flat.setHasContext(EncogFileSection.parseBoolean(params,
            BasicNetwork.TAG_HAS_CONTEXT));
        flat.setInputCount(EncogFileSection.parseInt(params,
            PersistConst.INPUT_COUNT));
        flat.setLayerCounts(EncogFileSection.parseIntArray(params,
            BasicNetwork.TAG_LAYER_COUNTS));
        flat.setLayerFeedCounts(EncogFileSection.parseIntArray(params,
            BasicNetwork.TAG_LAYER_FEED_COUNTS));
        flat.setLayerContextCount(EncogFileSection.parseIntArray(
            params, BasicNetwork.TAG_LAYER_CONTEXT_COUNT));
        flat.setLayerIndex(EncogFileSection.parseIntArray(params,
            BasicNetwork.TAG_LAYER_INDEX));
        flat.setLayerOutput(EncogFileSection.parseDoubleArray(params,
            PersistConst.OUTPUT));
        flat.setLayerSums(new double[flat.getLayerOutput().length]);
        flat.setOutputCount(EncogFileSection.parseInt(params,
            PersistConst.OUTPUT_COUNT));
        flat.setWeightIndex(EncogFileSection.parseIntArray(params,
            BasicNetwork.TAG_WEIGHT_INDEX));
        flat.setWeights(EncogFileSection.parseDoubleArray(params,
            PersistConst.WEIGHTS));
        flat.setBiasActivation(EncogFileSection.parseDoubleArray(
            params, BasicNetwork.TAG_BIAS_ACTIVATION));
      } else if (section.getSectionName().equals("RBF-NETWORK")
          && section.getSubSectionName().equals("ACTIVATION")) {
        int index = 0;

        flat.setActivationFunctions(new ActivationFunction[flat
            .getLayerCounts().length]);

        for (final String line : section.getLines()) {
          ActivationFunction af = null;
          final List<String> cols = EncogFileSection
              .splitColumns(line);
          final String name = "org.encog.engine.network.activation."
              + cols.get(0);
          try {
            final Class<?> clazz = Class.forName(name);
            af = (ActivationFunction) clazz.newInstance();
          } catch (final ClassNotFoundException e) {
            throw new PersistError(e);
          } catch (final InstantiationException e) {
            throw new PersistError(e);
          } catch (final IllegalAccessException e) {
            throw new PersistError(e);
          }

          for (int i = 0; i < af.getParamNames().length; i++) {
            af.setParam(i,
                CSVFormat.EG_FORMAT.parse(cols.get(i + 1)));
          }

          flat.getActivationFunctions()[index++] = af;
        }

      } else if (section.getSectionName().equals("RBF-NETWORK")
          && section.getSubSectionName().equals("RBF")) {
        int index = 0;

        final int hiddenCount = flat.getLayerCounts()[1];
        final int inputCount = flat.getLayerCounts()[2];

        flat.setRBF(new RadialBasisFunction[hiddenCount]);

        for (final String line : section.getLines()) {
          RadialBasisFunction rbf = null;
          final List<String> cols = EncogFileSection
              .splitColumns(line);
          final String name = "org.encog.mathutil.rbf." + cols.get(0);
          try {
            final Class<?> clazz = Class.forName(name);
            rbf = (RadialBasisFunction) clazz.newInstance();
          } catch (final ClassNotFoundException e) {
            throw new PersistError(e);
          } catch (final InstantiationException e) {
            throw new PersistError(e);
          } catch (final IllegalAccessException e) {
            throw new PersistError(e);
          }

          rbf.setWidth(CSVFormat.EG_FORMAT.parse(cols.get(1)));
          rbf.setPeak(CSVFormat.EG_FORMAT.parse(cols.get(2)));
          rbf.setCenters(new double[inputCount]);

          for (int i = 0; i < inputCount; i++) {
            rbf.getCenters()[i] = CSVFormat.EG_FORMAT.parse(cols
                .get(i + 3));
          }

          flat.getRBF()[index++] = rbf;
        }

      }
    }

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   */
  @Override
  public final 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]);
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  /**
   * Construct RBF network.
   */
  public RBFNetwork() {
    this.flat = new FlatNetworkRBF();
  }
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    // Set the standard RBF neuron width.
    // Literature seems to suggest this is a good default value.
    final double volumeNeuronWidth = 2.0 / hiddenCount;

    this.flat = new FlatNetworkRBF(inputCount, rbf.length, outputCount, rbf);

    try {
      // try this
      setRBFCentersAndWidthsEqualSpacing(-1, 1, t, volumeNeuronWidth,
          false);
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   * @param outputCount The output count.
   * @param rbf The RBF type.
   */
  public RBFNetwork(final int inputCount, final int outputCount,
      final RadialBasisFunction[] rbf) {
    this.flat = new FlatNetworkRBF(inputCount, rbf.length, outputCount, rbf);
    this.flat.setRBF(rbf);
  }
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   * {@inheritDoc}
   */
  @Override
  public Object read(final InputStream is) {
    final RBFNetwork result = new RBFNetwork();
    final FlatNetworkRBF flat = (FlatNetworkRBF) result.getFlat();

    final EncogReadHelper in = new EncogReadHelper(is);
    EncogFileSection section;

    while ((section = in.readNextSection()) != null) {
      if (section.getSectionName().equals("RBF-NETWORK")
          && section.getSubSectionName().equals("PARAMS")) {
        final Map<String, String> params = section.parseParams();
        result.getProperties().putAll(params);
      }
      if (section.getSectionName().equals("RBF-NETWORK")
          && section.getSubSectionName().equals("NETWORK")) {
        final Map<String, String> params = section.parseParams();

        flat.setBeginTraining(EncogFileSection.parseInt(params,
            BasicNetwork.TAG_BEGIN_TRAINING));
        flat.setConnectionLimit(EncogFileSection.parseDouble(params,
            BasicNetwork.TAG_CONNECTION_LIMIT));
        flat.setContextTargetOffset(EncogFileSection.parseIntArray(
            params, BasicNetwork.TAG_CONTEXT_TARGET_OFFSET));
        flat.setContextTargetSize(EncogFileSection.parseIntArray(
            params, BasicNetwork.TAG_CONTEXT_TARGET_SIZE));
        flat.setEndTraining(EncogFileSection.parseInt(params,
            BasicNetwork.TAG_END_TRAINING));
        flat.setHasContext(EncogFileSection.parseBoolean(params,
            BasicNetwork.TAG_HAS_CONTEXT));
        flat.setInputCount(EncogFileSection.parseInt(params,
            PersistConst.INPUT_COUNT));
        flat.setLayerCounts(EncogFileSection.parseIntArray(params,
            BasicNetwork.TAG_LAYER_COUNTS));
        flat.setLayerFeedCounts(EncogFileSection.parseIntArray(params,
            BasicNetwork.TAG_LAYER_FEED_COUNTS));
        flat.setLayerContextCount(EncogFileSection.parseIntArray(
            params, BasicNetwork.TAG_LAYER_CONTEXT_COUNT));
        flat.setLayerIndex(EncogFileSection.parseIntArray(params,
            BasicNetwork.TAG_LAYER_INDEX));
        flat.setLayerOutput(section.parseDoubleArray(params,
            PersistConst.OUTPUT));
        flat.setLayerSums(new double[flat.getLayerOutput().length]);
        flat.setOutputCount(EncogFileSection.parseInt(params,
            PersistConst.OUTPUT_COUNT));
        flat.setWeightIndex(EncogFileSection.parseIntArray(params,
            BasicNetwork.TAG_WEIGHT_INDEX));
        flat.setWeights(section.parseDoubleArray(params,
            PersistConst.WEIGHTS));
        flat.setBiasActivation(section.parseDoubleArray(
            params, BasicNetwork.TAG_BIAS_ACTIVATION));
      } else if (section.getSectionName().equals("RBF-NETWORK")
          && section.getSubSectionName().equals("ACTIVATION")) {
        int index = 0;

        flat.setActivationFunctions(new ActivationFunction[flat
            .getLayerCounts().length]);

        for (final String line : section.getLines()) {
          ActivationFunction af = null;
          final List<String> cols = EncogFileSection
              .splitColumns(line);
          final String name = "org.encog.engine.network.activation."
              + cols.get(0);
          try {
            final Class<?> clazz = Class.forName(name);
            af = (ActivationFunction) clazz.newInstance();
          } catch (final ClassNotFoundException e) {
            throw new PersistError(e);
          } catch (final InstantiationException e) {
            throw new PersistError(e);
          } catch (final IllegalAccessException e) {
            throw new PersistError(e);
          }

          for (int i = 0; i < af.getParamNames().length; i++) {
            af.setParam(i,
                CSVFormat.EG_FORMAT.parse(cols.get(i + 1)));
          }

          flat.getActivationFunctions()[index++] = af;
        }

      } else if (section.getSectionName().equals("RBF-NETWORK")
          && section.getSubSectionName().equals("RBF")) {
        int index = 0;

        final int hiddenCount = flat.getLayerCounts()[1];
        final int inputCount = flat.getLayerCounts()[2];

        flat.setRBF(new RadialBasisFunction[hiddenCount]);

        for (final String line : section.getLines()) {
          RadialBasisFunction rbf = null;
          final List<String> cols = EncogFileSection
              .splitColumns(line);
          final String name = "org.encog.mathutil.rbf." + cols.get(0);
          try {
            final Class<?> clazz = Class.forName(name);
            rbf = (RadialBasisFunction) clazz.newInstance();
          } catch (final ClassNotFoundException e) {
            throw new PersistError(e);
          } catch (final InstantiationException e) {
            throw new PersistError(e);
          } catch (final IllegalAccessException e) {
            throw new PersistError(e);
          }

          rbf.setWidth(CSVFormat.EG_FORMAT.parse(cols.get(1)));
          rbf.setPeak(CSVFormat.EG_FORMAT.parse(cols.get(2)));
          rbf.setCenters(new double[inputCount]);

          for (int i = 0; i < inputCount; i++) {
            rbf.getCenters()[i] = CSVFormat.EG_FORMAT.parse(cols
                .get(i + 3));
          }

          flat.getRBF()[index++] = rbf;
        }

      }
    }

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   */
  @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]);
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  /**
   * Construct RBF network.
   */
  public RBFNetwork() {
    this.flat = new FlatNetworkRBF();
  }
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    // Set the standard RBF neuron width.
    // Literature seems to suggest this is a good default value.
    final double volumeNeuronWidth = 2.0 / hiddenCount;

    this.flat = new FlatNetworkRBF(inputCount, rbf.length, outputCount, rbf);

    try {
      // try this
      setRBFCentersAndWidthsEqualSpacing(-1, 1, t, volumeNeuronWidth,
          false);
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   * @param rbf
   *            The RBF type.
   */
  public RBFNetwork(final int inputCount, final int outputCount,
      final RadialBasisFunction[] rbf) {
    this.flat = new FlatNetworkRBF(inputCount, rbf.length, outputCount, rbf);
    this.flat.setRBF(rbf);
  }
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Related Classes of org.encog.neural.flat.FlatNetworkRBF

  • org.encog.engine.network.activation.ActivationLinear
  • org.encog.neural.rbf.PersistRBFNetwork
  • org.encog.neural.rbf.RBFNetwork
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