Examples of org.encog.ml.svm.SVM

org.encog.ml.svm.SVM
This is a network that is backed by one or more Support Vector Machines (SVM). It is designed to function very similarly to an Encog neural network, and is largely interchangeable with an Encog neural network. The support vector machine supports several types. Regression is used when you want the network to predict a value, given the input. Function approximation is a good example of regression. Classification is used when you want the SVM to group the input data into one or more classes. Support Vector Machines typically have a single output. Neural networks can have multiple output neurons. To get around this issue, this class will create multiple SVM's if there is more than one output specified. Because a SVM is trained quite differently from a neural network, none of the neural network training classes will work. This class must be trained using SVMTrain.

    InputSVM dialog = new InputSVM((SVM) file.getObject());


    if (dialog.process()) {
      double c = dialog.getC().getValue();
      double g = dialog.getGamma().getValue();
      SVM method = (SVM) file.getObject();
      SVMTrain train = new SVMTrain((SVM) method, trainingData);
      train.setC(c);
      train.setGamma(g);
      train.iteration();
      double error = method.calculateError(trainingData);
      if (EncogWorkBench.askQuestion("Training Done",
          "Error: " + Format.formatPercent(error)
              + "\nSave training?")) {
        file.save();
      }

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  }


  private void performSVMSearch(ProjectEGFile file, MLDataSet trainingData) {
    InputSearchSVM dialog = new InputSearchSVM();
    SVM method = (SVM) file.getObject();


    dialog.getBeginningGamma().setValue(SVMTrain.DEFAULT_GAMMA_BEGIN);
    dialog.getEndingGamma().setValue(SVMTrain.DEFAULT_GAMMA_END);
    dialog.getStepGamma().setValue(SVMTrain.DEFAULT_GAMMA_STEP);
    dialog.getBeginningC().setValue(SVMTrain.DEFAULT_CONST_BEGIN);

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    if (outputCount != 1) {
      throw new EncogError("SVM can only have an output size of 1.");
    }


    final SVM result = new SVM(inputCount, svmType, kernelType);


    return result;
  }

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    report.beginHTML();
    report.title(title);
    report.beginBody();
    report.h1(title);
    report.beginTable();
    SVM svm = (SVM)encogObject.getObject();
    report.tablePair("Input Count",""+svm.getInputCount());
    report.tablePair("SVM Type",svm.getSVMType().toString());
    report.tablePair("Kernel Type",svm.getKernelType().toString());
    report.endTable();
    report.endBody();
    report.endHTML();
    
    this.display(report.toString());

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    return temp.process(this.normalizedSunspots);
  }
  
  public SVM createNetwork()
  {
    SVM network = new SVM(WINDOW_SIZE,true);
    return network;
  }

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  }
  
  public void run()
  {
    normalizeSunspots(0.1,0.9);
    SVM network = createNetwork();
    MLDataSet training = generateTraining();
    train(network,training);
    predict(network);
    
  }

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    if (outputCount != 1) {
      throw new EncogError("SVM can only have an output size of 1.");
    }


    final SVM result = new SVM(inputCount, svmType, kernelType);


    return result;
  }

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  public final File SERIAL_FILENAME = TEMP_DIR.createFile("encogtest.ser");
  
  private SVM create()
  {
    MLDataSet training = new BasicMLDataSet(XOR.XOR_INPUT,XOR.XOR_IDEAL);
    SVM result = new SVM(2,SVMType.EpsilonSupportVectorRegression,KernelType.RadialBasisFunction);
    final SVMTrain train = new SVMTrain(result, training);
    train.iteration();
    return result;
  }

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    return result;
  }
  
  public void testPersistEG()
  {
    SVM network = create();


    EncogDirectoryPersistence.saveObject((EG_FILENAME), network);
    SVM network2 = (SVM)EncogDirectoryPersistence.loadObject((EG_FILENAME));
    validate(network2);
  }

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    validate(network2);
  }
  
  public void testPersistSerial() throws IOException, ClassNotFoundException
  {
    SVM network = create();
    
    SerializeObject.save(SERIAL_FILENAME, network);
    SVM network2 = (SVM)SerializeObject.load(SERIAL_FILENAME);
        
    validate(network2);
  }

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Related Classes of org.encog.ml.svm.SVM

org.encog.EncogError

org.encog.examples.neural.predict.sunspot.PredictSunspotSVM

org.encog.mathutil.libsvm.svm_node

org.encog.mathutil.libsvm.svm_parameter

org.encog.ml.data.basic.BasicMLData

org.encog.ml.data.MLData

org.encog.ml.factory.method.SVMFactory

org.encog.ml.factory.TestMLMethodFactory

org.encog.ml.svm.training.search.SVMSearchJob

org.encog.neural.NeuralNetworkError

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