Examples of org.encog.ml.data.basic.BasicMLData

org.encog.ml.data.basic.BasicMLData
Basic implementation of the MLData interface that stores the data in an array. @author jheaton

    final int totalWindowSize = this.inputWindow + this.predictWindow;
    final int stopPoint = data.length - totalWindowSize;


    for (int i = 0; i < stopPoint; i++) {
      final MLData inputData 
        = new BasicMLData(this.inputWindow);
      final MLData idealData 
        = new BasicMLData(this.predictWindow);


      int index = i;


      // handle input window
      for (int j = 0; j < this.inputWindow; j++) {
        inputData.setData(j, data[index++]);
      }


      // handle predict window
      for (int j = 0; j < this.predictWindow; j++) {
        idealData.setData(j, data[index++]);
      }


      final MLDataPair pair = new BasicMLDataPair(inputData,
          idealData);
      result.add(pair);

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        int totalWindowSize = inputWindow + predictWindow;
        int stopPoint = data.length - totalWindowSize;


        for (int i = 0; i < stopPoint; i++)
        {
            MLData inputData = new BasicMLData(inputWindow);
            MLData idealData = new BasicMLData(predictWindow);


            int index = i;


            // handle input window
            for (int j = 0; j < inputWindow; j++)
            {
                inputData.setData(j, data[index++]);
            }


            // handle predict window
            for (int j = 0; j < predictWindow; j++)
            {
                idealData.setData(j, data[index++]);
            }


            pair = new BasicMLDataPair(inputData, idealData);
        }
        return pair;

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  public MLDataPair get(int index) {
    if (index > size()) {
      return null;
    }


    BasicMLData input = new BasicMLData(
        MatrixMLDataSet.this.calculatedInputSize * calculateLagCount());
    BasicMLData ideal = new BasicMLData(
        MatrixMLDataSet.this.calculatedIdealSize * calculateLeadCount());
    MLDataPair pair = new BasicMLDataPair(input, ideal);


    MatrixMLDataSet.this.getRecord(index, pair);

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   * @param multiplier
   *            How many input vectors.
   * @return The data element.
   */
  public MLData allocateInputVector(int multiplier) {
    return new BasicMLData(calculateNormalizedInputCount() * multiplier);
  }

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   */
  @Override
  public double calculateScore(final MLMethod genome) {
    final EncogProgram prg = (EncogProgram) genome;
    final PrgPopulation pop = (PrgPopulation) prg.getPopulation();
    final MLData inputData = new BasicMLData(pop.getContext()
        .getDefinedVariables().size());
    prg.compute(inputData);
    return 0;
  }

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    while (csv.next()) {
      MLData input = null;
      MLData ideal = null;
      int index = 0;


      input = new BasicMLData(inputSize);
      for (int i = 0; i < inputSize; i++) {
        double d = csv.getDouble(index++);
        input.setData(i, d);
      }


      if (idealSize > 0) {
        ideal = new BasicMLData(idealSize);
        for (int i = 0; i < idealSize; i++) {
          double d = csv.getDouble(index++);
          ideal.setData(i, d);
        }
      }

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   */
  @Override
  public MLData compute(final MLData input) {


    // Allocate result
    final MLData result = new BasicMLData(this.outputLayer.size());


    // Copy the input
    for (int i = 0; i < input.size(); i++) {
      this.inputLayer.setActivation(i, input.getData(i));
    }


    // Request calculation of outputs
    for (int i = 0; i < this.outputLayer.size(); i++) {
      final FreeformNeuron outputNeuron = this.outputLayer.getNeurons()
          .get(i);
      outputNeuron.performCalculation();
      result.setData(i, outputNeuron.getActivation());
    }


    updateContext();


    return result;

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    while (csv.next() && !shouldStop()) {
      updateStatus(true);
      final double[] inputArray = AnalystNormalizeCSV.extractFields(
          analyst, this.analystHeaders, csv, outputLength, true);
      final MLData input = new BasicMLData(inputArray);
      this.data.add(new BasicMLDataPair(input));


      recordCount++;
    }
    setRecordCount(recordCount);

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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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   * 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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