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.

  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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  @Override
  public final MLMethod generate() {
    if (this.outputNeurons != 1) {
      throw new PatternError("A SVM may only have one output.");
    }
    final SVM network = new SVM(this.inputNeurons, this.svmType,
        this.kernelType);
    return network;
  }

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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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  public void finishTraining() {
    stop();
  }


  private SVM generateSVM() {
    final SVM svm = new SVM(this.modelSVM.getInputCount(),
        this.modelSVM.getSVMType(), this.modelSVM.getKernelType());
    return svm;
  }

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   * This method creates, and trains, a SVM with the best const and gamma.
   * @return The best SVM. 
   */
  @Override
  public MLMethod getMethod() {
    final SVM result = generateSVM();
    result.getParams().C = this.bestConst;
    result.getParams().gamma = this.bestGamma;
    result.setModel(svm.svm_train(this.problem, result.getParams()));
    return result;
  }

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  public Object requestNextTask() {
    if (this.done || getShouldStop()) {
      return null;
    }


    final SVM svm = generateSVM();


    // advance
    this.currentConst += this.constStep;
    if (this.currentConst > this.constEnd) {
      this.currentConst = this.constBegin;

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  }
  
  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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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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