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

      final double newWeight = temp + ((i - this.center))
          * stepSize[row];


      this.network.getFlat().getWeights()[weight] = newWeight;


      final MLData output = this.network.compute(inputData);
      points[i] = output.getData(outputNeuron);      
    }


    double result = 0.0;
    for (int i = 0; i < this.dCoeff.length; i++) {
      result += this.dCoeff[i] * points[i];

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    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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  /**
   * {@inheritDoc}
   */
  public double distance(MLData d)
  {
    MLData diff = value.minus(d);
    double sum = 0.;
    
    for (int i = 0; i < diff.size(); i++)
      sum += diff.getData(i) * diff.getData(i);
  
    return Math.sqrt(sum);
  }

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   * {@inheritDoc}
   */
  @Override
  public double distance(MLDataPair d)
  {
    MLData diff = value.minus(d.getInput());
    double sum = 0.;
    
    for (int i = 0; i < diff.size(); i++)
      sum += diff.getData(i) * diff.getData(i);
  
    return Math.sqrt(sum);
  }

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  public static MLDataSet loadCSVTOMemory(CSVFormat format,
      String filename, boolean headers, int inputSize, int idealSize) {
    MLDataSet result = new BasicMLDataSet();
    ReadCSV csv = new ReadCSV(filename, headers, format);
    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);
        }
      }


      MLDataPair pair = new BasicMLDataPair(input, ideal);
      result.add(pair);

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

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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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    }


    double worstDistance = Double.NEGATIVE_INFINITY;


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


      // determine winner
      final int winner = EngineArray.indexOfLargest(out.getData());


      // calculate the distance
      double distance = 0;
      for (int i = 0; i < pair.getInput().size(); i++) {
        final double diff = pair.getInput().getData(i)

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