org.encog.ml.svm.training
Class SVMTrain

java.lang.Object
  extended by org.encog.ml.train.BasicTraining
      extended by org.encog.ml.svm.training.SVMTrain
All Implemented Interfaces:
MLTrain

public class SVMTrain
extends BasicTraining

Provides training for Support Vector Machine networks.


Constructor Summary
SVMTrain(SVM method, MLDataSet dataSet)
          Construct a trainer for an SVM network.
 
Method Summary
 boolean canContinue()
          
 double getC()
           
 int getFold()
           
 double getGamma()
           
 MLMethod getMethod()
          Get the current best machine learning method from the training.
 svm_problem getProblem()
           
 boolean isTrainingDone()
           
 void iteration()
          Perform either a train or a cross validation.
 TrainingContinuation pause()
          Pause the training to continue later.
 void resume(TrainingContinuation state)
          Resume training.
 void setC(double theC)
          Set the constant C.
 void setFold(int theFold)
          Set the number of folds.
 void setGamma(double theGamma)
          Set the gamma.
 
Methods inherited from class org.encog.ml.train.BasicTraining
addStrategy, finishTraining, getError, getImplementationType, getIteration, getStrategies, getTraining, iteration, postIteration, preIteration, setError, setIteration, setTraining
 
Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

Constructor Detail

SVMTrain

public SVMTrain(SVM method,
                MLDataSet dataSet)
Construct a trainer for an SVM network.

Parameters:
method - The network to train.
dataSet - The training data for this network.
Method Detail

canContinue

public boolean canContinue()

Returns:
True if the training can be paused, and later continued.

getC

public double getC()
Returns:
The constant C.

getFold

public int getFold()
Returns:
the fold

getGamma

public double getGamma()
Returns:
The gamma.

getMethod

public MLMethod getMethod()
Get the current best machine learning method from the training.

Returns:
The best machine learningm method.

getProblem

public svm_problem getProblem()
Returns:
The problem being trained.

isTrainingDone

public boolean isTrainingDone()
Specified by:
isTrainingDone in interface MLTrain
Overrides:
isTrainingDone in class BasicTraining
Returns:
True if the training is done.

iteration

public void iteration()
Perform either a train or a cross validation. If the folds property is greater than 1 then cross validation will be done. Cross validation does not produce a usable model, but it does set the error. If you are cross validating try C and Gamma values until you have a good error rate. Then use those values to train, producing the final model.


pause

public final TrainingContinuation pause()
Pause the training to continue later.

Returns:
A training continuation object.

resume

public void resume(TrainingContinuation state)
Resume training.

Parameters:
state - The training continuation object to use to continue.

setC

public void setC(double theC)
Set the constant C.

Parameters:
theC - The constant C.

setFold

public void setFold(int theFold)
Set the number of folds.

Parameters:
theFold - the fold to set.

setGamma

public void setGamma(double theGamma)
Set the gamma.

Parameters:
theGamma - The new gamma.


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