libsvm.svm.svm_train()方法的使用及代码示例

x33g5p2x  于2022-01-30 转载在 其他  
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本文整理了Java中libsvm.svm.svm_train()方法的一些代码示例，展示了svm.svm_train()的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。svm.svm_train()方法的具体详情如下：
包路径：libsvm.svm
类名称：svm
方法名：svm_train

svm.svm_train介绍

暂无

代码示例

代码示例来源：origin: prestodb/presto

private static Callable<svm_model> getTrainingFunction(svm_problem problem, svm_parameter param)
{
  return () -> svm.svm_train(problem, param);
}

代码示例来源：origin: datumbox/datumbox-framework

svm_model model = svm.svm_train(prob, params);

代码示例来源：origin: prestosql/presto

private static Callable<svm_model> getTrainingFunction(svm_problem problem, svm_parameter param)
{
  return () -> svm.svm_train(problem, param);
}

代码示例来源：origin: pl.edu.icm.yadda/yadda-analysis-impl

public void buildClassifier(List<TrainingElement<BxZoneLabel>> trainingElements) 
{
  assert trainingElements.size() > 0;
  if(features == null) {
    features = (String[])trainingElements.get(0).getObservation().getFeatureNames().toArray(new String[1]);
  }
  scaler.setFeatureLimits(trainingElements);
  problem = buildDatasetForTraining(trainingElements);
  model = libsvm.svm.svm_train(problem, param);
}

代码示例来源：origin: eu.fbk.utils/utils-svm

private static Classifier trainJava(final Parameters parameters,
    final Iterable<LabelledVector> trainingSet) throws IOException {
  // Prepare the svm_parameter object based on supplied parameters
  final svm_parameter parameter = encodeParameters(parameters);
  // Encode the training set as an svm_problem object, filling a dictionary meanwhile
  final Dictionary<String> dictionary = Dictionary.create();
  final svm_problem problem = encodeProblem(dictionary, trainingSet);
  // Perform training
  final svm_model model = svm.svm_train(problem, parameter);
  // Compute model hash, by saving and reloading SVM model
  final File tmpFile = File.createTempFile("svm", ".bin");
  tmpFile.deleteOnExit();
  svm.svm_save_model(tmpFile.getAbsolutePath(), model);
  final String modelString = com.google.common.io.Files.toString(tmpFile,
      Charset.defaultCharset());
  final String modelHash = computeHash(dictionary, modelString);
  final svm_model reloadedModel = svm
      .svm_load_model(new BufferedReader(new StringReader(modelString)));
  tmpFile.delete();
  // Build and return the SVM object
  return new LibSvmClassifier(parameters, modelHash, dictionary, reloadedModel);
}

代码示例来源：origin: jzy3d/jzy3d-api

public void train(Vector<svm_node[]> vx, Vector<Double> vy, Parameters parameters){
  this.parameters = parameters;
  this.param = parameters.getParam();
  
  load(vx, vy);
  model = svm.svm_train(prob,param);
  parameters.setParam( param );
}

代码示例来源：origin: dkpro/dkpro-tc

public void run(String argv[]) throws Exception
{
  parse_command_line(argv);
  read_problem();
  error_msg = svm.svm_check_parameter(prob, param);
  if (error_msg != null) {
    throw new Exception(error_msg);
  }
  model = svm.svm_train(prob, param);
  svm.svm_save_model(model_file_name, model);
}

代码示例来源：origin: org.dkpro.tc/dkpro-tc-ml-libsvm

public void run(String argv[]) throws Exception
{
  parse_command_line(argv);
  read_problem();
  error_msg = svm.svm_check_parameter(prob, param);
  if (error_msg != null) {
    throw new Exception(error_msg);
  }
  model = svm.svm_train(prob, param);
  svm.svm_save_model(model_file_name, model);
}

代码示例来源：origin: ch.epfl.bbp.nlp/bluima_jsre

public void run(File input_file, File model_file, double c, int mem, double[] weight) throws IOException
{
  input_file_name = input_file.getAbsolutePath();
  model_file_name = model_file.getAbsolutePath();
  //System.out.println("input_file_name: " + input_file_name);
  //System.out.println("model_file_name: " + model_file_name);
  //System.out.println("mem: " + mem);
  set_param(c, mem, weight);
  read_problem();
  error_msg = svm.svm_check_parameter(prob,param);
  if(error_msg != null)
  {
    System.err.print("Error: "+error_msg+"\n");
    System.exit(1);
  }
  if(cross_validation != 0)
  {
    //do_cross_validation();
  }
  else
  {
    model = svm.svm_train(prob,param);
    svm.svm_save_model(model_file_name, model);
  }
}

代码示例来源：origin: DigitalPebble/TextClassification

public void internal_learn() throws Exception {
  // dumps a file with the vectors for the documents
  File learningFile = new File(this.vector_location);
  // make space
  parse_command_line();
  if (cross_validation && nfold < 2)
    throw new Exception("n-fold cross validation: n must >= 2\n");
  read_problem(learningFile);
  error_msg = svm.svm_check_parameter(prob, param);
  if (error_msg != null) {
    System.err.print("Error: " + error_msg + "\n");
    throw new Exception(error_msg);
  }
  if (cross_validation) {
    do_cross_validation();
  } else {
    model = svm.svm_train(prob, param);
    svm.svm_save_model(model_file_name, model);
  }
}

代码示例来源：origin: ClearTK/cleartk

private void run(String argv[]) throws IOException {
 parse_command_line(argv);
 read_problem();
 error_msg = svm.svm_check_parameter(prob, param);
 if (error_msg != null) {
  System.err.print("ERROR: " + error_msg + "\n");
  System.exit(1);
 }
 if (cross_validation != 0) {
  do_cross_validation();
 } else {
  model = svm.svm_train(prob, param);
  svm.svm_save_model(model_file_name, model);
 }
}

代码示例来源：origin: org.cleartk/cleartk-ml-libsvm

private void run(String argv[]) throws IOException {
 parse_command_line(argv);
 read_problem();
 error_msg = svm.svm_check_parameter(prob, param);
 if (error_msg != null) {
  System.err.print("ERROR: " + error_msg + "\n");
  System.exit(1);
 }
 if (cross_validation != 0) {
  do_cross_validation();
 } else {
  model = svm.svm_train(prob, param);
  svm.svm_save_model(model_file_name, model);
 }
}

代码示例来源：origin: education-service/speech-mfcc

public svm_model trainModel(Dataset dataset) {
  List<Observation> observations = dataset.getObservations();
  svm_problem learningProblem = new svm_problem();
  int dataCount = observations.size();
  learningProblem.y = new double[dataCount];
  learningProblem.l = dataCount;
  learningProblem.x = new svm_node[dataCount][];
  for (int i = 0; i < dataCount; i++) {
    List<Double> features = observations.get(i).getFeatures();
    learningProblem.x[i] = new svm_node[features.size()];
    for (int j = 0; j < features.size(); j++) {
      svm_node node = new svm_node();
      node.index = j + 1;
      node.value = features.get(j);
      learningProblem.x[i][j] = node;
    }
    learningProblem.y[i] = dataset.getClassCode(observations.get(i));
  }
  svm_parameter param = new svm_parameter();
  param.probability = 1;
  param.gamma = 0.5;
  param.nu = 0.5;
  param.C = 1;
  param.svm_type = svm_parameter.C_SVC;
  param.kernel_type = svm_parameter.LINEAR;
  param.cache_size = 20000;
  param.eps = 0.0001;
  svm_model model = svm.svm_train(learningProblem, param);
  return model;
}

代码示例来源：origin: openimaj/openimaj

/**
 *    {@inheritDoc}
 *     @see org.openimaj.ml.training.BatchTrainer#train(java.util.List)
 */
@Override
public void train( final List<? extends Annotated<OBJECT, ANNOTATION>> data )
{
  // Check the data has 2 classes and update the class map.
  if( this.checkInputDataOK( data ) )
  {
    // Setup the SVM problem
    final svm_parameter param = SVMAnnotator.getDefaultSVMParameters();
    final svm_problem prob = this.getSVMProblem( data, param, this.extractor );
    // Train the SVM
    this.model = libsvm.svm.svm_train( prob, param );
    // Save the model if we're going to do that.
    if( this.saveModel != null ) try
    {
      svm.svm_save_model( this.saveModel.getAbsolutePath(), this.model );
    }
    catch( final IOException e )
    {
      e.printStackTrace();
    }
  }
}

代码示例来源：origin: chungkwong/MathOCR

svm_parameter modified=(svm_parameter)parameter.clone();
  modified.gamma=1.0/variables;
  SvmModel m=new SvmModel(svm.svm_train(problem,modified),names);
  return m;
}else{
  return new SvmModel(svm.svm_train(problem,parameter),names);

代码示例来源：origin: org.maochen.nlp/CoreNLP-NLP

@Override
public IClassifier train(List<Tuple> trainingData) {
  if (para == null) {
    LOG.warn("Parameter is null. Use the default parameter.");
    this.para = getDefaultPara();
  }
  labelIndexer = new LabelIndexer(trainingData);
  svm_problem prob = new svm_problem();
  int featSize = trainingData.iterator().next().vector.getVector().length;
  prob.l = trainingData.size();
  prob.y = new double[prob.l];
  prob.x = new svm_node[prob.l][featSize];
  for (int i = 0; i < trainingData.size(); i++) {
    Tuple tuple = trainingData.get(i);
    prob.x[i] = new svm_node[featSize];
    for (int j = 0; j < tuple.vector.getVector().length; j++) {
      svm_node node = new svm_node();
      node.index = j;
      node.value = tuple.vector.getVector()[j];
      prob.x[i][j] = node;
    }
    prob.y[i] = labelIndexer.getIndex(tuple.label);
  }
  model = svm.svm_train(prob, para);
  return this;
}

代码示例来源：origin: org.clulab/processors

System.setOut(NoPrintStream.NO_PRINTSTREAM);
System.setErr(NoPrintStream.NO_PRINTSTREAM);
svm_model model = svm.svm_train(prob, param);
System.setOut(err);
System.setOut(out);

代码示例来源：origin: org.maltparser/maltparser

System.setOut(NoPrintStream.NO_PRINTSTREAM);
System.setErr(NoPrintStream.NO_PRINTSTREAM);
svm_model model = svm.svm_train(prob, param);
System.setOut(err);
System.setOut(out);

代码示例来源：origin: net.sf.tweety/machinelearning

@Override
public SupportVectorMachine train(TrainingSet<DefaultObservation, DoubleCategory> trainingSet, ParameterSet params) {
  if(!params.containsParameter(C_PARAMETER) || !params.containsParameter(GAMMA_PARAMETER))
    throw new IllegalArgumentException("Parameters missing.");
  svm_parameter param = new svm_parameter();
  //TODO the following properties should be parameterized as well
  // Type of SVM
  param.svm_type = svm_parameter.C_SVC;
  // Kernel type (leave it at RBF for now)
  param.kernel_type = svm_parameter.RBF;
  // stopping criteria
  param.eps = 0.001;
  // cache size of kernel
  param.cache_size = 256;
  // do not set penalties for specific classes
  param.nr_weight = 0;
  // Given parameters
  // gamma parameter of RBF kernel
  param.gamma = params.getParameter(GAMMA_PARAMETER).getValue();
  // C parameter of RBF kernel
  param.C = params.getParameter(C_PARAMETER).getValue();
      
  return new SupportVectorMachine(svm.svm_train(trainingSet.toLibsvmProblem(), param));
}

代码示例来源：origin: chungkwong/MathOCR

problem.x[i][1].value=i-6;
svm_model model=svm.svm_train(problem,parameter);
svm_node[] unknown=new svm_node[]{
  new svm_node(),

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