Examples of org.encog.ml.data.MLDataPair

org.encog.ml.data.MLDataPair
Training data is stored in two ways, depending on if the data is for supervised, or unsupervised training. For unsupervised training just an input value is provided, and the ideal output values are null. For supervised training both input and the expected ideal outputs are provided. This interface abstracts classes that provide a holder for both of these two data items. @author jheaton

    BasicMLData ideal = new BasicMLData(1);
    ImageNeuralData input = new ImageNeuralData(image);
    set.add(input,ideal);
    set.downsample(2,2);
    Iterator<MLDataPair> itr = set.iterator();
    MLDataPair pair = (MLDataPair)itr.next();
    MLData data = pair.getInput();
    double[] d = data.getData();
    //Assert.assertEquals(d[0],-1.0, 0.1);
    //Assert.assertEquals(d[5],1, 0.1);
    
    // just "flex" these for no exceptions

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      MLDataSet result = new BasicMLDataSet();
      for(int i=0;i<count;i++) {
        for(int j=0;j<4;j++) {
          MLData inputData = new BasicMLData(XOR_INPUT[j]);
          MLData idealData = new BasicMLData(XOR_IDEAL[j]);
          MLDataPair pair = new BasicMLDataPair(inputData,idealData);
          inputData.setData(0, inputData.getData(0)+RangeRandomizer.randomize(-0.1, 0.1));
          inputData.setData(1, inputData.getData(1)+RangeRandomizer.randomize(-0.1, 0.1));
          result.add(pair);
        }
      }

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   *            The size of the ideal data.
   * @return A new data pair object.
   */
  public static MLDataPair createPair(final int inputSize, 
      final int idealSize) {
    MLDataPair result;


    if (idealSize > 0) {
      result = new BasicMLDataPair(new BasicMLData(inputSize),
          new BasicMLData(idealSize));
    } else {

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   * {@inheritDoc}
   */
  @Override
  public final void add(final MLData inputData, final MLData idealData) {


    final MLDataPair pair = new BasicMLDataPair(inputData, idealData);
    this.data.add(pair);
  }

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  @Override
  public final int getIdealSize() {
    if (this.data.isEmpty()) {
      return 0;
    }
    final MLDataPair first = this.data.get(0);
    if (first.getIdeal() == null) {
      return 0;
    }


    return first.getIdeal().size();
  }

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  @Override
  public final int getInputSize() {
    if (this.data.isEmpty()) {
      return 0;
    }
    final MLDataPair first = this.data.get(0);
    return first.getInput().size();
  }

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   * {@inheritDoc}
   */
  @Override
  public final void getRecord(final long index, final MLDataPair pair) {


    final MLDataPair source = this.data.get((int) index);
    pair.setInputArray(source.getInputArray());
    if (pair.getIdealArray() != null) {
      pair.setIdealArray(source.getIdealArray());
    }


  }

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  public void refresh() {
    int selected = this.list.getSelectedIndex();
    if( selected==-1 )
      return;
    
    MLDataPair pair = BasicMLDataPair.createPair(this.data.getInputSize(), this.data.getIdealSize());
    this.data.getRecord(selected, pair);
    int gridHeight;
    int gridWidth;
    
    try {
      gridHeight = Integer.parseInt(this.fieldHeight.getText());
      if( gridHeight<=0 ) 
        throw new NumberFormatException();
    } catch(NumberFormatException ex) {
      EncogWorkBench.displayError("Error", "Invalid height.");
      return;
    }
    
    try {
      gridWidth = Integer.parseInt(this.fieldWidth.getText());
      if( gridHeight<=0 ) 
        throw new NumberFormatException();
    } catch(NumberFormatException ex) {
      EncogWorkBench.displayError("Error", "Invalid width.");
      return;
    }
    
    this.grid.updateData(gridHeight,gridWidth,pair.getInput().getData());
  }

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      int input = dialog.getInput().getValue();
      int output = dialog.getIdeal().getValue();


      BufferedNeuralDataSet trainingData = new BufferedNeuralDataSet(file);


      MLDataPair pair = BasicMLDataPair.createPair(input,
          output);
      trainingData.beginLoad(input, output);
      for (int i = 0; i < elements; i++) {
        trainingData.add(pair);
      }

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    } else if (field instanceof InputFieldMLDataSet) {
      final InputFieldMLDataSet neuralField = 
        (InputFieldMLDataSet) field;
      final MLDataFieldHolder holder = this.dataSetFieldMap
          .get(field);
      final MLDataPair pair = holder.getPair();
      int offset = neuralField.getOffset();
      if (offset < pair.getInput().size()) {
        result = pair.getInput().getData(offset);
      } else {
        offset -= pair.getInput().size();
        result = pair.getIdeal().getData(offset);
      }
    } else {
      result = field.getValue(index);
    }

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