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

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.DAGTrainSM
                  de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGTrainSM

All Implemented Interfaces:

InstantiableFromParameterSet, SequenceScore, StatisticalModel, TrainableStatisticalModel, Storable, Cloneable

Direct Known Subclasses:

FSDAGModelForGibbsSampling

public class FSDAGTrainSM
extends DAGTrainSM

This class can be used for any discrete fixed structure directed acyclic graphical model ( FSDAGTrainSM).

Author:

Jens Keilwagen

Field Summary

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

constraints

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

FSDAGTrainSM(FSDAGTrainSMParameterSet params)
This is the main constructor.

FSDAGTrainSM(StringBuffer xml)
The standard constructor for the interface Storable.

Method Summary

void	drawParameters(DataSet data, double[] weights, int[][] graph) This method draws the parameters of the model from the a posteriori density.
String	getInstanceName() Should return a short instance name such as iMM(0), BN(2), ...
byte	getMaximalMarkovOrder() This method returns the maximal used Markov order, if possible.
String	getStructure() Returns a String representation of the underlying graph.
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.
void	train(DataSet data, double[] weights, int[][] graph) Computes the model with structure graph.
static void	train(TrainableStatisticalModel[] models, int[][] graph, double[][] weights, DataSet... data) Computes the models with structure graph.

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

checkAcyclic, clone, createConstraints, drawParameters, emitDataSet, estimateParameters, getFurtherModelInfos, getLogPriorTerm, getLogProbFor, getNumericalCharacteristics, setFurtherModelInfos, toString

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, train

Methods inherited from class java.lang.Object

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

Constructor Detail

FSDAGTrainSM

public FSDAGTrainSM(FSDAGTrainSMParameterSet params)
             throws CloneNotSupportedException,
                    IllegalArgumentException,
                    NonParsableException

This is the main constructor. It creates a new FSDAGTrainSM from the given FSDAGTrainSMParameterSet.

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:

DAGTrainSM.DAGTrainSM(de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.parameters.IDGTrainSMParameterSet)

FSDAGTrainSM

public FSDAGTrainSM(StringBuffer xml)
             throws NonParsableException

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

Parameters:

xml - the XML representation as StringBuffer

Throws:

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

See Also:

Storable, DAGTrainSM.DAGTrainSM(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

getMaximalMarkovOrder

public byte getMaximalMarkovOrder()

Description copied from interface: StatisticalModel

This method returns the maximal used Markov order, if possible.

Specified by:

getMaximalMarkovOrder in interface StatisticalModel

Overrides:

getMaximalMarkovOrder in class AbstractTrainableStatisticalModel

Returns:

maximal used Markov order

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()

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 sample 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 sample do not match)

See Also:

DataSet.getElementAt(int), DataSet.ElementEnumerator

train

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

Computes the model with structure graph.

Parameters:

data - the DataSet

weights - the weights for the sequences in the DataSet

graph - the graph

Throws:

Exception - if something went wrong

drawParameters

public void drawParameters(DataSet data,
                           double[] weights,
                           int[][] graph)
                    throws Exception

This method draws the parameters of the model from the a posteriori density. For drawing from the prior you have to set the data and their weights to null. Furthermore this method enables you to specify a new graph structure.

Parameters:

data - a DataSet or null

weights - the (positive) weights for each sequence of the DataSet or null

graph - the graph or null for the current graph

Throws:

Exception - if something went wrong

See Also:

DAGTrainSM.drawParameters(DataSet, double[]), DAGTrainSM.checkAcyclic(int, int[][])

train

public static void train(TrainableStatisticalModel[] models,
                         int[][] graph,
                         double[][] weights,
                         DataSet... data)
                  throws Exception

Computes the models with structure graph.

Parameters:

models - an array of AbstractTrainableStatisticalModels containing only instances of FSDAGTrainSM

data - the DataSet

weights - the weights for the sequences in the DataSet

graph - the graph

Throws:

Exception - if something went wrong

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

getStructure

public String getStructure()

Description copied from class: InhomogeneousDGTrainSM

Returns a String representation of the underlying graph.

Overrides:

getStructure in class DAGTrainSM

Returns:

a String representation of the underlying graph