Examples of org.encog.neural.NeuralNetworkError

org.encog.neural.NeuralNetworkError
Used by the neural network classes to indicate an error.

    validateNeuron(fromLayer + 1, toNeuron);
    final int fromLayerNumber = getLayerCount() - fromLayer - 1;
    final int toLayerNumber = fromLayerNumber - 1;


    if (toLayerNumber < 0) {
      throw new NeuralNetworkError(
          "The specified layer is not connected to another layer: "
              + fromLayer);
    }


    final int weightBaseIndex

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   * @param value The bias activation.
   */
  public final void setLayerBiasActivation(final int l, 
        final double value) {
    if (!isLayerBiased(l)) {
      throw new NeuralNetworkError(
          "Error, the specified layer does not have a bias: " + l);
    }


    this.structure.requireFlat();
    final int layerNumber = getLayerCount() - l - 1;

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    this.structure.requireFlat();
    final int fromLayerNumber = getLayerCount() - fromLayer - 1;
    final int toLayerNumber = fromLayerNumber - 1;


    if (toLayerNumber < 0) {
      throw new NeuralNetworkError(
          "The specified layer is not connected to another layer: "
              + fromLayer);
    }


    final int weightBaseIndex

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   * @param targetLayer The target layer.
   * @param neuron The target neuron.
   */
  public final void validateNeuron(final int targetLayer, final int neuron) {
    if ((targetLayer < 0) || (targetLayer >= getLayerCount())) {
      throw new NeuralNetworkError("Invalid layer count: " + targetLayer);
    }


    if ((neuron < 0) || (neuron >= getLayerTotalNeuronCount(targetLayer))) {
      throw new NeuralNetworkError("Invalid neuron number: " + neuron);
    }
  }

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   */
  public MLData compute(final MLData input) {
    final MLData result = new BasicMLData(this.outputCount);


    if (this.neurons.size() == 0) {
      throw new NeuralNetworkError(
"This network has not been evolved yet, it has no neurons in the NEAT synapse.");
    }


    int flushCount = 1;

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   *            The new neuron count for this layer.
   */
  public final void changeNeuronCount(final int layer, final int neuronCount) {


    if (neuronCount == 0) {
      throw new NeuralNetworkError("Can't decrease to zero neurons.");
    }


    final int currentCount = this.network.getLayerNeuronCount(layer);


    // is there anything to do?

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   *            The pattern to train for.
   */
  public final void addPattern(final MLData pattern) {


    if (pattern.size() != getNeuronCount()) {
      throw new NeuralNetworkError("Network with " + getNeuronCount()
          + " neurons, cannot learn a pattern of size "
          + pattern.size());
    }


    // Create a row matrix from the input, convert boolean to bipolar

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  private void increaseNeuronCount(final int targetLayer,
      final int neuronCount) {


    // check for errors
    if (targetLayer > this.network.getLayerCount()) {
      throw new NeuralNetworkError("Invalid layer " + targetLayer);
    }


    if (neuronCount <= 0) {
      throw new NeuralNetworkError("Invalid neuron count " + neuronCount);
    }


    final int oldNeuronCount = this.network
        .getLayerNeuronCount(targetLayer);
    final int increaseBy = neuronCount - oldNeuronCount;


    if (increaseBy <= 0) {
      throw new NeuralNetworkError(
          "New neuron count is either a decrease or no change: "
              + neuronCount);
    }


    // access the flat network

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    // check for errors
    this.network.validateNeuron(targetLayer, neuron);


    // don't empty a layer
    if (this.network.getLayerNeuronCount(targetLayer) <= 1) {
      throw new NeuralNetworkError(
          "A layer must have at least a single neuron.  If you want to remove the entire layer you must create a new network.");
    }


    // access the flat network
    final FlatNetwork flat = this.network.getStructure().getFlat();

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   */
  public final void addWeight(final int fromNeuron, final int toNeuron,
      final double value) {
    final int index = (toNeuron * this.neuronCount) + fromNeuron;
    if (index >= this.weights.length) {
      throw new NeuralNetworkError("Out of range: fromNeuron:"
          + fromNeuron + ", toNeuron: " + toNeuron);
    }
    this.weights[index] += value;
  }

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Related Classes of org.encog.neural.NeuralNetworkError

org.encog.ml.factory.method.PNNFactory

org.encog.ml.factory.method.RBFNetworkFactory

org.encog.ml.svm.SVM

org.encog.neural.art.ART1

org.encog.neural.bam.BAM

org.encog.neural.cpn.training.TrainInstar

org.encog.neural.neat.NEATCODEC

org.encog.neural.neat.NEATNetwork

org.encog.neural.neat.NEATPopulation

org.encog.neural.neat.training.NEATGenome

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