Examples of Svm

com.nr.ci.Svm
Support Vector Machines Copyright (C) Numerical Recipes Software 1986-2007 Java translation Copyright (C) Huang Wen Hui 2012 @author hwh
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.

Examples of org.encog.ml.svm.SVM

  }
  
  public void testFactorySVM() {
    String architecture = "?->C(KERNEL=RBF,TYPE=NEW)->?";
    MLMethodFactory factory = new MLMethodFactory();
    SVM network = (SVM)factory.create(MLMethodFactory.TYPE_SVM, architecture, 4, 1);
    Assert.assertEquals(4,network.getInputCount());
    Assert.assertEquals(1,network.getOutputCount());
  }

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Examples of org.encog.ml.svm.SVM

    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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Examples of org.encog.ml.svm.SVM


  }


  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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Examples of org.encog.ml.svm.SVM


    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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Examples of org.encog.ml.svm.SVM

    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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Examples of org.encog.ml.svm.SVM

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

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Examples of org.encog.ml.svm.SVM

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

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Examples of org.encog.ml.svm.SVM


    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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Examples of org.encog.ml.svm.SVM

  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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Examples of org.encog.ml.svm.SVM

    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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