Examples of org.encog.neural.NeuralNetworkError

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

   *            The input to the network.
   * @return The output from the network.
   */
  public MLData compute(final MLData input) {
    if (!(input instanceof BiPolarNeuralData)) {
      throw new NeuralNetworkError(
          "Input to ART1 logic network must be BiPolarNeuralData.");
    }


    final BiPolarNeuralData output = new BiPolarNeuralData(this.f1Count);
    compute((BiPolarNeuralData) input, output);

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    this.outputCount = outputCount;


    setNEATActivationFunction(new ActivationSteepenedSigmoid());


    if (populationSize == 0) {
      throw new NeuralNetworkError(
          "Population must have more than zero genomes.");
    }


  }

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   * Approximate the weights based on the input values.
   */
  private void initWeights() {


    if (this.training.getRecordCount() != this.network.getInstarCount()) {
      throw new NeuralNetworkError(
          "If the weights are to be set from the " 
          + "training data, then there must be one instar " 
          + "neuron for each training element.");
    }

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      this.priors = new double[getOutputCount()];


      for (final MLDataPair pair : samples) {
        final int i = (int) pair.getIdeal().getData(0);
        if (i >= this.countPer.length) {
          throw new NeuralNetworkError(
              "Training data contains more classes than neural network has output neurons to hold.");
        }
        this.countPer[i]++;
      }

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        .getNeuronsChromosome();
    final List<NEATLinkGene> linksChromosome = neatGenome
        .getLinksChromosome();


    if (neuronsChromosome.get(0).getNeuronType() != NEATNeuronType.Bias) {
      throw new NeuralNetworkError(
          "The first neuron must be the bias neuron, this genome is invalid.");
    }


    final List<NEATLink> links = new ArrayList<NEATLink>();
    final ActivationFunction[] afs = new ActivationFunction[neuronsChromosome

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   *            The pattern to train for.
   */
  public 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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   */
  public 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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   * @param output The toutpu
   */
  public void init(final int neuronCount, final double[] weights,
      final double[] output) {
    if (neuronCount != output.length) {
      throw new NeuralNetworkError("Neuron count(" + neuronCount
          + ") must match output count(" + output.length + ").");
    }


    if ((neuronCount * neuronCount) != weights.length) {
      throw new NeuralNetworkError("Weight count(" + weights.length
          + ") must be the square of the neuron count(" + neuronCount
          + ").");
    }


    this.neuronCount = neuronCount;

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      try {
        this.connectionLimited = true;
        this.connectionLimit = Double.parseDouble(limit);
        enforceLimit();
      } catch (final NumberFormatException e) {
        throw new NeuralNetworkError("Invalid property("
            + BasicNetwork.TAG_LIMIT + "):" + limit);
      }
    } else {
      this.connectionLimited = false;
      this.connectionLimit = 0;

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   * property.
   */
  public final void finalizeStructure() {


    if (this.layers.size() < 2) {
      throw new NeuralNetworkError(
          "There must be at least two layers before the structure is finalized.");
    }


    final FlatLayer[] flatLayers = new FlatLayer[this.layers.size()];

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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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