Examples of MLMethod

com.heatonresearch.aifh.learning.MLMethod
This interface defines a Machine Learning Method. A MLMethod is an algorithm that accepts data and provides some sort of insight into it. This could be a neural network, support vector machine, clustering algorithm, or something else entirely.
org.encog.ml.MLMethod
This interface is the base for all Encog Machine Learning methods. It defines very little, other than the fact that a subclass is a Machine Learning Method. A MLMethod is an algorithm that accepts data and provides some sort of insight into it. This could be a neural network, support vector machine, clustering algorithm, or something else entirely. Many MLMethods must be trained by a MLTrain object before they are useful.

Examples of com.heatonresearch.aifh.learning.MLMethod

    /**
     * Perform the task.
     */
    @Override
    public void run() {
        MLMethod phenotype = this.owner.getCodec().decode(this.genome);
        if (phenotype != null) {
            double score;
            try {
                score = this.scoreFunction.calculateScore(phenotype);
            } catch (AIFHError e) {

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Examples of com.heatonresearch.aifh.learning.MLMethod

     */
    @Override
    public void calculateScore(final Genome g) {


        // decode
        final MLMethod phenotype = getCODEC().decode(g);
        double score;


        // deal with invalid decode
        if (phenotype == null) {
            if (getBestComparator().shouldMinimize()) {

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Examples of org.encog.ml.MLMethod

  public void performEvaluate() {
    try {
      EvaluateDialog dialog = new EvaluateDialog(EncogWorkBench
          .getInstance().getMainWindow());
      if (dialog.process()) {
        MLMethod method = dialog.getNetwork();
        MLDataSet training = dialog.getTrainingSet();


        double error = 0;


        if (method instanceof MLError) {
          error = ((MLError) method).calculateError(training);
          EncogWorkBench.displayMessage("Error For this Network", ""
              + Format.formatPercent(error));


        } else {
          EncogWorkBench.displayError("Error",
              "The Machine Learning method "
                  + method.getClass().getSimpleName()
                  + " does not support error calculation.");
        }
      }
    } catch (Throwable t) {
      EncogWorkBench.displayError("Error Evaluating Network", t);

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Examples of org.encog.ml.MLMethod

      while (!this.cancel) {
        this.iteration++;
        this.lastError = this.train.getError();


        if (this.resetOption.get() != -1) {
          MLMethod method = null;
          
          if( getEncogObject() instanceof ProjectEGFile ) {
            method = (MLMethod)((ProjectEGFile)getEncogObject()).getObject();
          }

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Examples of org.encog.ml.MLMethod

    
    MLDataSet trainingData = generateTraining(INPUT_OUTPUT_COUNT, COMPL);
    
    for(int i=0;i<TRIES;i++) {
    
      MLMethod method = EncogUtility.simpleFeedForward(INPUT_OUTPUT_COUNT,
          HIDDEN_COUNT, 0, INPUT_OUTPUT_COUNT, false);
      
      Propagation train = new Backpropagation((ContainsFlat)method, trainingData,1.7,0);
      //Propagation train = new ResilientPropagation((ContainsFlat)method, trainingData);
      ((Propagation)train).fixFlatSpot(true);

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Examples of org.encog.ml.MLMethod

  
  public void process(String methodName, String methodArchitecture,String trainerName, String trainerArgs,int outputNeurons) {
    
    // first, create the machine learning method
    MLMethodFactory methodFactory = new MLMethodFactory();    
    MLMethod method = methodFactory.create(methodName, methodArchitecture, 2, outputNeurons);
    
    // second, create the data set    
    MLDataSet dataSet = new BasicMLDataSet(XOR_INPUT, XOR_IDEAL);
    
    // third, create the trainer

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Examples of org.encog.ml.MLMethod

  }


  public void reportTraining(MLTrain train) {
    // reset if needed
    if (this.resetOption.get() != -1) {
      MLMethod method = this.analyst.getMethod();
            
      if( method==null )
      {
        this.resetOption.set(-1);
        EncogWorkBench.displayError("Error", "This machine learning method cannot be reset or randomized.");

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Examples of org.encog.ml.MLMethod

        .getMainWindow());
    if (mlMethod != null)
      dialog.setMethod(mlMethod);


    if (dialog.process()) {
      MLMethod method = dialog.getNetwork();
      MLDataSet trainingData = dialog.getTrainingSet();


      if (method == null) {
        EncogWorkBench.displayError("Error",
            "Machine language method is required to train.");
        return;
      }


      if (method instanceof ART1) {
        EncogWorkBench
            .displayError("Error",
                "ART1 Networks are not trained, they learn as they are queried.");
        return;
      }


      if (trainingData == null) {
        EncogWorkBench.displayError("Error",
            "Training set is required to train.");
        return;
      }


      if (method instanceof HopfieldNetwork) {
        HopfieldNetwork hp = (HopfieldNetwork) method;
        ProjectEGFile file = (ProjectEGFile) dialog.getComboNetwork()
            .getSelectedValue();
        for (MLDataPair pair : trainingData) {
          hp.addPattern(pair.getInput());
        }
        if (EncogWorkBench.askQuestion("Hopfield",
            "Training done, save?")) {
          file.save();
        }
      } else if (method instanceof SOM) {
        ProjectEGFile file = (ProjectEGFile) dialog.getComboNetwork()
            .getSelectedValue();
        performSOM(file, trainingData);
      } else if (method instanceof SVM) {
        ProjectEGFile file = (ProjectEGFile) dialog.getComboNetwork()
            .getSelectedValue();
        performSVM(file, trainingData);
      } else if (method instanceof CPN) {
        ProjectEGFile file = (ProjectEGFile) dialog.getComboNetwork()
            .getSelectedValue();
        performCPN(file, trainingData);
      } else if (method instanceof BasicNetwork || method instanceof RBFNetwork ) {


        ChooseBasicNetworkTrainingMethod choose = new ChooseBasicNetworkTrainingMethod(
            EncogWorkBench.getInstance().getMainWindow(),method);
        if (choose.process()) {
          ProjectEGFile file = (ProjectEGFile) dialog
              .getComboNetwork().getSelectedValue();


          switch (choose.getType()) {
          case SCG:
            performSCG(file, trainingData);
            break;
          case PropagationResilient:
            performRPROP(file, trainingData);
            break;
          case PropagationBack:
            performBPROP(file, trainingData);
            break;
          case PropagationManhattan:
            performManhattan(file, trainingData);
            break;
          case LevenbergMarquardt:
            performLMA(file, trainingData);
            break;
          case Genetic:
            performGenetic(file, trainingData);
            break;
          case Annealing:
            performAnnealing(file, trainingData);
            break;
          case ADALINE:
            performADALINE(file, trainingData);
            break;
          case PropagationQuick:
            performQPROP(file, trainingData);
            break;
          }
        }
      } else {
        EncogWorkBench.displayError("Unknown Method",
            "No training method is available for: "
                + method.getClass().getName());
      }
    }
  }

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Examples of org.encog.ml.MLMethod

import org.encog.workbench.tabs.incremental.IncrementalPruneTab;


public class CreateNeuralNetwork {


  public static void process(File path) {
    MLMethod network = null;
    CreateNeuralNetworkDialog dialog = new CreateNeuralNetworkDialog(
        EncogWorkBench.getInstance().getMainWindow());
    dialog.setType(NeuralNetworkType.Feedforward);
    if (dialog.process()) {
      switch (dialog.getType()) {

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Examples of org.encog.ml.MLMethod

   */
  public final MLMethod create(final String methodType,
      final String architecture, final int input, final int output) {
    for (EncogPluginBase plugin : Encog.getInstance().getPlugins()) {
      if (plugin instanceof EncogPluginService1) {
        MLMethod result = ((EncogPluginService1) plugin).createMethod(
            methodType, architecture, input, output);
        if (result != null) {
          return result;
        }
      }

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