Examples of org.encog.ml.data.buffer.BufferedNeuralDataSet

org.encog.ml.data.buffer.BufferedNeuralDataSet
This class is not memory based, so very long files can be used, without running out of memory. This dataset uses a Encog binary training file as a buffer. When used with a slower access dataset, such as CSV, XML or SQL, where parsing must occur, this dataset can be used to load from the slower dataset and train at much higher speeds. This class makes use of Java file channels for maximum file access performance. If you are going to create a binary file, by using the add methods, you must call beginLoad to cause Encog to open an output file. Once the data has been loaded, call endLoad. You can also use the BinaryDataLoader class, with a CODEC, to load many other popular external formats. The binary files produced by this class are in the Encog binary training format, and can be used with any Encog platform. Encog binary files are stored using "little endian" numbers.

   */
  public MLDataSet getTrainingSet() {
    if( this.getComboTraining().getSelectedValue()==null )      
      return null;
    File file = ((ProjectTraining)this.getComboTraining().getSelectedValue()).getFile();
    BufferedNeuralDataSet result = new BufferedNeuralDataSet(file);
    if( this.loadToMemory.getValue())
      return result.loadToMemory();
    else
      return result;
  }

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   */
  public MLDataSet getTrainingSet() {
    if( this.comboTraining.getSelectedValue()==null )      
      return null;
    File file = ((ProjectTraining)this.comboTraining.getSelectedValue()).getFile();
    BufferedNeuralDataSet result = new BufferedNeuralDataSet(file);
    return result;
  }

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    if (dialog.process()) {
      int elements = dialog.getElements().getValue();
      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);
      }
      trainingData.endLoad();
    }
  }

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