Package org.encog.ml.factory

Examples of org.encog.ml.factory.MLTrainFactory

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    // second, create the data set   
    MLDataSet dataSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
   
    // third, create the trainer
    MLTrainFactory trainFactory = new MLTrainFactory(); 
    MLTrain train = trainFactory.create(method,dataSet,trainerName,trainerArgs);       
    // reset if improve is less than 1% over 5 cycles
    if( method instanceof MLResettable && !(train instanceof ManhattanPropagation) ) {
      train.addStrategy(new RequiredImprovementStrategy(500));
    }
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   * @return The trainer.
   */
  private MLTrain createTrainer(final MLMethod method,
      final MLDataSet trainingSet) {

    final MLTrainFactory factory = new MLTrainFactory();

    final String type = getProp().getPropertyString(
        ScriptProperties.ML_TRAIN_TYPE);
    final String args = getProp().getPropertyString(
        ScriptProperties.ML_TRAIN_ARGUMENTS);
   
    EncogLogging.log(EncogLogging.LEVEL_DEBUG, "training type:" + type);
    EncogLogging.log(EncogLogging.LEVEL_DEBUG, "training args:" + args);
   
    if( method instanceof MLResettable ) {
      this.getAnalyst().setMethod(method);
    }


    MLTrain train = factory.create(method, trainingSet, type, args);

    if (this.kfold > 0) {
      train = new CrossValidationKFold(train, this.kfold);
    }

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   * @return The trainer.
   */
  private MLTrain createTrainer(final MLMethod method,
      final MLDataSet trainingSet) {

    final MLTrainFactory factory = new MLTrainFactory();

    final String type = getProp().getPropertyString(
        ScriptProperties.ML_TRAIN_TYPE);
    final String args = getProp().getPropertyString(
        ScriptProperties.ML_TRAIN_ARGUMENTS);
   
    EncogLogging.log(EncogLogging.LEVEL_DEBUG, "training type:" + type);
    EncogLogging.log(EncogLogging.LEVEL_DEBUG, "training args:" + args);
   
    if( method instanceof MLResettable ) {
      this.getAnalyst().setMethod(method);
    }


    MLTrain train = factory.create(method, trainingSet, type, args);

    if ( getKfold() > 0) {
      train = new CrossValidationKFold(train, getKfold() );
    }

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    if (this.trainingType == null) {
      throw new EncogError(
          "Please call selectTraining first to choose how to train.");
    }
    MLTrainFactory trainFactory = new MLTrainFactory();
    MLTrain train = trainFactory.create(method, dataset, this.trainingType,
        this.trainingArgs);
    return train;
  }
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Related Classes of org.encog.ml.factory.MLTrainFactory

  • org.encog.app.analyst.commands.CmdTrain
  • org.encog.EncogError
  • org.encog.examples.neural.xor.XORFactory
  • org.encog.ml.model.EncogModel
  • org.encog.ml.train.MLTrain
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