de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
Class FSMEManager

java.lang.Object
  de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
      de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM
          de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM
              de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEManager
                  de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSMEManager

All Implemented Interfaces:

InstantiableFromParameterSet, SequenceScore, StatisticalModel, TrainableStatisticalModel, Storable, Cloneable

public class FSMEManager
extends MEManager

This class can be used for any discrete fixed structure maximum entropy model (FSMEM).

Author:

Jens Keilwagen

Field Summary

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEManager

factors

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM

DEFAULT_STREAM, sostream

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM

params, trained

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel

alphabets, length

Constructor Summary

FSMEManager(FSMEMParameterSet params)
Creates a new MEManager from a given MEManagerParameterSet.

FSMEManager(StringBuffer stringBuff)
The standard constructor for the interface Storable.

Method Summary

String	getInstanceName() Should return a short instance name such as iMM(0), BN(2), ...
protected String	getXMLTag() Returns the XML tag that is used for this model in DiscreteGraphicalTrainSM.fromXML(StringBuffer) and DiscreteGraphicalTrainSM.toXML().
protected void	set(DGTrainSMParameterSet params, boolean trained) Sets the parameters as internal parameters and does some essential computations.
void	train(DataSet data, double[] weights) Trains the TrainableStatisticalModel object given the data as DataSet using the specified weights.

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.MEManager

clone, emitDataSet, getFactors, getFactors, getFurtherModelInfos, getLogPriorTerm, getLogProbFor, getNumericalCharacteristics, getStructure, setFurtherModelInfos, toString, trainFactors

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.InhomogeneousDGTrainSM

check, setOutputStream

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.discrete.DiscreteGraphicalTrainSM

fromXML, getCurrentParameterSet, getDescription, getESS, isInitialized, toXML

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel

getAlphabetContainer, getCharacteristics, getLength, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getMaximalMarkovOrder, toString, train

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

FSMEManager

public FSMEManager(FSMEMParameterSet params)
            throws CloneNotSupportedException,
                   IllegalArgumentException,
                   NonParsableException

Creates a new MEManager from a given MEManagerParameterSet.

Parameters:

params - the given parameter set

Throws:

CloneNotSupportedException - if the parameter set could not be cloned

IllegalArgumentException - if the parameter set is not instantiated

NonParsableException - if the parameter set is not parsable

See Also:

MEManager.MEManager(MEManagerParameterSet)

FSMEManager

public FSMEManager(StringBuffer stringBuff)
            throws NonParsableException

The standard constructor for the interface Storable. Creates a new FSMEManager out of its XML representation.

Parameters:

stringBuff - the XML representation as StringBuffer

Throws:

NonParsableException - if the FSMEManager could not be reconstructed out of the XML representation (the StringBuffer could not be parsed)

See Also:

Storable, MEManager.MEManager(StringBuffer)

Method Detail

getInstanceName

public String getInstanceName()

Description copied from interface: SequenceScore

Should return a short instance name such as iMM(0), BN(2), ...

Returns:

a short instance name

train

public void train(DataSet data,
                  double[] weights)
           throws Exception

Description copied from interface: TrainableStatisticalModel

Trains the TrainableStatisticalModel object given the data as DataSet using the specified weights. The weight at position i belongs to the element at position i. So the array weight should have the number of sequences in the data set as dimension. (Optionally it is possible to use weight == null if all weights have the value one.)
This method should work non-incrementally. That means the result of the following series: train(data1); train(data2) should be a fully trained model over data2 and not over data1+data2. All parameters of the model were given by the call of the constructor.

Parameters:

data - the given sequences as DataSet

weights - the weights of the elements, each weight should be non-negative

Throws:

Exception - if the training did not succeed (e.g. the dimension of weights and the number of sequences in the data set do not match)

See Also:

DataSet.getElementAt(int), DataSet.ElementEnumerator

getXMLTag

protected String getXMLTag()

Description copied from class: DiscreteGraphicalTrainSM

Returns the XML tag that is used for this model in DiscreteGraphicalTrainSM.fromXML(StringBuffer) and DiscreteGraphicalTrainSM.toXML().

Specified by:

getXMLTag in class DiscreteGraphicalTrainSM

Returns:

the XML tag that is used in DiscreteGraphicalTrainSM.fromXML(StringBuffer) and DiscreteGraphicalTrainSM.toXML()

See Also:

DiscreteGraphicalTrainSM.fromXML(StringBuffer), DiscreteGraphicalTrainSM.toXML()

set

protected void set(DGTrainSMParameterSet params,
                   boolean trained)
            throws CloneNotSupportedException,
                   NonParsableException

Description copied from class: DiscreteGraphicalTrainSM

Sets the parameters as internal parameters and does some essential computations. Used in fromParameterSet-methods.

Overrides:

set in class InhomogeneousDGTrainSM

Parameters:

params - the new ParameterSet

trained - indicates if the model is trained or not

Throws:

CloneNotSupportedException - if the parameter set could not be cloned

NonParsableException - if the parameters of the model could not be parsed