Package com.clearnlp.classification.train

Examples of com.clearnlp.classification.train.AbstractTrainSpace

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    catch (Exception e) {e.printStackTrace();}
  }
 
  public void train(String trainFile, String modelFile, byte vectorType, int labelCutoff, int featureCutoff, int numThreads, byte solver, double cost, double eps, double bias) throws Exception
  {
    AbstractTrainSpace space = null;
    boolean hasWeight = AbstractTrainSpace.hasWeight(vectorType, trainFile);
   
    switch (vectorType)
    {
    case AbstractTrainSpace.VECTOR_SPARSE:
      space = new SparseTrainSpace(hasWeight); break;
    case AbstractTrainSpace.VECTOR_STRING:
      space = new StringTrainSpace(hasWeight, labelCutoff, featureCutoff); break;
    }
   
    space.readInstances(UTInput.createBufferedFileReader(trainFile));
    space.build();
   
    AbstractModel model = getModel(space, numThreads, solver, cost, eps, bias);
    ObjectOutputStream out = new ObjectOutputStream(new BufferedOutputStream(new FileOutputStream(modelFile)));
   
    out.writeObject(model);
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    catch (Exception e) {e.printStackTrace();}
  }
 
  public void train(String trainFile, String modelFile, byte vectorType, int labelCutoff, int featureCutoff, byte solver, double alpha, double rho, double eps, boolean average) throws Exception
  {
    AbstractTrainSpace space = null;
    boolean hasWeight = AbstractTrainSpace.hasWeight(vectorType, trainFile);
   
    switch (vectorType)
    {
    case AbstractTrainSpace.VECTOR_SPARSE:
      space = new SparseTrainSpace(hasWeight); break;
    case AbstractTrainSpace.VECTOR_STRING:
      space = new StringTrainSpace(hasWeight, labelCutoff, featureCutoff); break;
    }
   
    space.readInstances(UTInput.createBufferedFileReader(trainFile));
    space.build();
   
    AbstractModel model = getModel(space, solver, alpha, rho, eps, average);
    ObjectOutputStream out = new ObjectOutputStream(new BufferedOutputStream(new FileOutputStream(modelFile)));
   
    out.writeObject(model);
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    catch (Exception e) {e.printStackTrace();}
  }
 
  public void train(String trainFile, String modelFile, byte vectorType, int labelCutoff, int featureCutoff, byte solver, double alpha, double rho, double eps) throws Exception
  {
    AbstractTrainSpace space = null;
    boolean hasWeight = AbstractTrainSpace.hasWeight(vectorType, trainFile);
   
    switch (vectorType)
    {
    case AbstractTrainSpace.VECTOR_SPARSE:
      space = new SparseTrainSpace(hasWeight); break;
    case AbstractTrainSpace.VECTOR_STRING:
      space = new StringTrainSpace(hasWeight, labelCutoff, featureCutoff); break;
    }
   
    space.readInstances(UTInput.createBufferedFileReader(trainFile));
    space.build();
   
    AbstractModel model = getModel(space, solver, alpha, rho, eps);
    ObjectOutputStream out = new ObjectOutputStream(new BufferedOutputStream(new FileOutputStream(modelFile)));
   
    out.writeObject(model);
View Full Code Here
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    catch (Exception e) {e.printStackTrace();}
  }
 
  public void train(String trainFile, String modelFile, byte vectorType, int labelCutoff, int featureCutoff, int numThreads, byte solver, double cost, double eps, double bias) throws Exception
  {
    AbstractTrainSpace space = null;
    boolean hasWeight = AbstractTrainSpace.hasWeight(vectorType, trainFile);
   
    switch (vectorType)
    {
    case AbstractTrainSpace.VECTOR_SPARSE:
      space = new SparseTrainSpace(hasWeight); break;
    case AbstractTrainSpace.VECTOR_STRING:
      space = new StringTrainSpace(hasWeight, labelCutoff, featureCutoff); break;
    }
   
    space.readInstances(UTInput.createBufferedFileReader(trainFile));
    space.build();
   
    AbstractModel model = getModel(space, numThreads, solver, cost, eps, bias);
    ObjectOutputStream out = new ObjectOutputStream(new BufferedOutputStream(new FileOutputStream(modelFile)));
   
    out.writeObject(model);
View Full Code Here
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    catch (Exception e) {e.printStackTrace();}
  }
 
  public void train(String trainFile, String modelFile, byte vectorType, int labelCutoff, int featureCutoff, byte solver, double alpha, double rho, double eps) throws Exception
  {
    AbstractTrainSpace space = null;
    boolean hasWeight = AbstractTrainSpace.hasWeight(vectorType, trainFile);
   
    switch (vectorType)
    {
    case AbstractTrainSpace.VECTOR_SPARSE:
      space = new SparseTrainSpace(hasWeight); break;
    case AbstractTrainSpace.VECTOR_STRING:
      space = new StringTrainSpace(hasWeight, labelCutoff, featureCutoff); break;
    }
   
    space.readInstances(UTInput.createBufferedFileReader(trainFile));
    space.build();
   
    AbstractModel model = getModel(space, solver, alpha, rho, eps);
    ObjectOutputStream out = new ObjectOutputStream(new BufferedOutputStream(new FileOutputStream(modelFile)));
   
    out.writeObject(model);
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Related Classes of com.clearnlp.classification.train.AbstractTrainSpace

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  • com.clearnlp.run.AdaGradTrain
  • com.clearnlp.run.LiblinearTrain
  • java.io.BufferedReader
  • java.io.LineNumberReader
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