Examples of org.encog.ml.data.MLData

org.encog.ml.data.MLData
Defines an array of data. This is an array of double values that could be used either for input data, actual output data or ideal output data. @author jheaton

  /**
   * {@inheritDoc}
   */
  @Override
  public MLData compute(final MLData input) {
    final MLData temp = computeInstar(input);
    return computeOutstar(temp);
  }

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   * Compute the instar layer.
   * @param input The input.
   * @return The output.
   */
  public MLData computeInstar(final MLData input) {
    final MLData result = new BasicMLData(this.instarCount);
    int w, i, j;
    double sum, sumWinners, maxOut;
    int winner = 0;
    final boolean[] winners = new boolean[this.instarCount];


    for (i = 0; i < this.instarCount; i++) {
      sum = 0;
      for (j = 0; j < this.inputCount; j++) {
        sum += this.weightsInputToInstar.get(j, i) * input.getData(j);
      }
      result.setData(i, sum);
      winners[i] = false;
    }
    sumWinners = 0;
    for (w = 0; w < this.winnerCount; w++) {
      maxOut = Double.MIN_VALUE;
      for (i = 0; i < this.instarCount; i++) {
        if (!winners[i] && (result.getData(i) > maxOut)) {
          winner = i;
          maxOut = result.getData(winner);
        }
      }
      winners[winner] = true;
      sumWinners += result.getData(winner);
    }
    for (i = 0; i < this.instarCount; i++) {
      if (winners[i]
          && (Math.abs(sumWinners) > Encog.DEFAULT_DOUBLE_EQUAL)) {
        result.getData()[i] /= sumWinners;
      } else {
        result.getData()[i] = 0;
      }
    }


    return result;
  }

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    }


    final ErrorCalculation error = new ErrorCalculation();


    for (final MLDataPair pair : this.training) {
      final MLData out = this.network.computeInstar(pair.getInput());


      final int j = EngineArray.indexOfLargest(out.getData());
      for (int i = 0; i < this.network.getOutstarCount(); i++) {
        final double delta = this.learningRate
            * (pair.getIdeal().getData(i) - this.network
                .getWeightsInstarToOutstar().get(j, i));
        this.network.getWeightsInstarToOutstar().add(j, i, delta);
      }


      final MLData out2 = this.network.computeOutstar(out);
      error.updateError(out2.getData(), pair.getIdeal().getData(), pair.getSignificance());
    }


    setError(error.calculate());
  }

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   * Compute the outstar layer.
   * @param input The input.
   * @return The output.
   */
  public MLData computeOutstar(final MLData input) {
    final MLData result = new BasicMLData(this.outstarCount);


    double sum = 0;


    for (int i = 0; i < this.outstarCount; i++) {
      sum = 0;
      for (int j = 0; j < this.instarCount; j++) {
        sum += this.weightsInstarToOutstar.get(j, i) * input.getData(j);
      }
      result.setData(i, sum);
    }
    return result;
  }

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   *            The input to this synapse.
   * @return The output from this synapse.
   */
  @Override
  public MLData compute(final MLData input) {
    final MLData result = new BasicMLData(this.outputCount);


    // clear from previous
    EngineArray.fill(this.preActivation, 0.0);
    EngineArray.fill(this.postActivation, 0.0);
    this.postActivation[0] = 1.0;


    // copy input
    EngineArray.arrayCopy(input.getData(), 0, this.postActivation, 1,
        this.inputCount);


    // iterate through the network activationCycles times
    for (int i = 0; i < this.activationCycles; ++i) {
      internalCompute();
    }


    // copy output
    EngineArray.arrayCopy(this.postActivation, this.outputIndex,
        result.getData(), 0, this.outputCount);


    return result;
  }

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  /**
   * {@inheritDoc}
   */
  @Override
  public int classify(MLData input) {
    MLData output = compute(input);
    return EngineArray.maxIndex(output.getData());
  }

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  protected void processPureBatch() {
    final ErrorCalculation errorCalc = new ErrorCalculation();
    this.visited.clear();


    for (final MLDataPair pair : this.training) {
      final MLData input = pair.getInput();
      final MLData ideal = pair.getIdeal();
      final MLData actual = this.network.compute(input);
      final double sig = pair.getSignificance();


      errorCalc.updateError(actual.getData(), ideal.getData(), sig);


      for (int i = 0; i < this.network.getOutputCount(); i++) {
        final double diff = (ideal.getData(i) - actual.getData(i))
            * sig;
        final FreeformNeuron neuron = this.network.getOutputLayer()
            .getNeurons().get(i);
        calculateOutputDelta(neuron, diff);
        calculateNeuronGradient(neuron);

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    int lastLearn = 0;
    final ErrorCalculation errorCalc = new ErrorCalculation();
    this.visited.clear();


    for (final MLDataPair pair : this.training) {
      final MLData input = pair.getInput();
      final MLData ideal = pair.getIdeal();
      final MLData actual = this.network.compute(input);
      final double sig = pair.getSignificance();


      errorCalc.updateError(actual.getData(), ideal.getData(), sig);


      for (int i = 0; i < this.network.getOutputCount(); i++) {
        final double diff = (ideal.getData(i) - actual.getData(i))
            * sig;
        final FreeformNeuron neuron = this.network.getOutputLayer()
            .getNeurons().get(i);
        calculateOutputDelta(neuron, diff);
        calculateNeuronGradient(neuron);

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          && section.getSubSectionName().equals("SAMPLES")) {
        for (final String line : section.getLines()) {
          final List<String> cols = EncogFileSection
              .splitColumns(line);
          int index = 0;
          final MLData inputData = new BasicMLData(inputCount);
          for (int i = 0; i < inputCount; i++) {
            inputData.setData(i,
                CSVFormat.EG_FORMAT.parse(cols.get(index++)));
          }
          final MLData idealData = new BasicMLData(outputCount);
          idealData.setData(0,CSVFormat.EG_FORMAT.parse(cols.get(index++)));
          final MLDataPair pair = new BasicMLDataPair(inputData,idealData);
          samples.add(pair);
        }
      }
    }

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    bmu.reset();


    // Determine the BMU for each training element.
    for (final MLDataPair pair : data) {
      final MLData input = pair.getInput();
      bmu.calculateBMU(input);
    }


    // update the error
    return bmu.getWorstDistance() / 100.0;

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