de.jstacs.models.discrete.inhomogeneous
Class FSDAGModel

java.lang.Object
  de.jstacs.models.AbstractModel
      de.jstacs.models.discrete.DiscreteGraphicalModel
          de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM
              de.jstacs.models.discrete.inhomogeneous.DAGModel
                  de.jstacs.models.discrete.inhomogeneous.FSDAGModel

All Implemented Interfaces:

InstantiableFromParameterSet, Model, Storable, Cloneable

Direct Known Subclasses:

FSDAGModelForGibbsSampling

public class FSDAGModel
extends DAGModel

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

Author:

Jens Keilwagen

Field Summary

Fields inherited from class de.jstacs.models.discrete.inhomogeneous.DAGModel

constraints

Fields inherited from class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM

DEFAULT_STREAM, sostream

Fields inherited from class de.jstacs.models.discrete.DiscreteGraphicalModel

params, trained

Fields inherited from class de.jstacs.models.AbstractModel

alphabets, length

Constructor Summary

FSDAGModel(FSDAGMParameterSet params)
This is the main constructor.

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

Method Summary

void	drawParameters(Sample 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 DiscreteGraphicalModel.fromXML(StringBuffer) and DiscreteGraphicalModel.toXML().
protected void	set(DGMParameterSet params, boolean trained) Sets the parameters as internal parameters and does some essential computations.
static void	train(Model[] models, int[][] graph, double[][] weights, Sample... data) Computes the models with structure graph.
void	train(Sample data, double[] weights) Trains the Model object given the data as Sample using the specified weights.
void	train(Sample data, double[] weights, int[][] graph) Computes the model with structure graph.

Methods inherited from class de.jstacs.models.discrete.inhomogeneous.DAGModel

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

Methods inherited from class de.jstacs.models.discrete.inhomogeneous.InhomogeneousDGM

check, setOutputStream

Methods inherited from class de.jstacs.models.discrete.DiscreteGraphicalModel

fromXML, getCurrentParameterSet, getDescription, getESS, isTrained, toXML

Methods inherited from class de.jstacs.models.AbstractModel

getAlphabetContainer, getCharacteristics, getLength, getLogProbFor, getLogProbFor, getLogProbFor, getLogProbFor, getPriorTerm, getProbFor, getProbFor, set, setNewAlphabetContainerInstance, train

Methods inherited from class java.lang.Object

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

Constructor Detail

FSDAGModel

public FSDAGModel(FSDAGMParameterSet params)
           throws CloneNotSupportedException,
                  IllegalArgumentException,
                  NonParsableException

This is the main constructor. It creates a new FSDAGModel from the given FSDAGMParameterSet.

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:

DAGModel.DAGModel(de.jstacs.models.discrete.inhomogeneous.parameters.IDGMParameterSet)

FSDAGModel

public FSDAGModel(StringBuffer xml)
           throws NonParsableException

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

Parameters:

xml - the XML representation as StringBuffer

Throws:

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

See Also:

Storable, DAGModel.DAGModel(StringBuffer)

Method Detail

getInstanceName

public String getInstanceName()

Description copied from interface: Model

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: Model

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

Specified by:

getMaximalMarkovOrder in interface Model

Overrides:

getMaximalMarkovOrder in class AbstractModel

Returns:

maximal used Markov order

getXMLTag

protected String getXMLTag()

Description copied from class: DiscreteGraphicalModel

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

Specified by:

getXMLTag in class DiscreteGraphicalModel

Returns:

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

See Also:

DiscreteGraphicalModel.fromXML(StringBuffer), DiscreteGraphicalModel.toXML()

train

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

Description copied from interface: Model

Trains the Model object given the data as Sample 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 Sample

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:

Sample.getElementAt(int), Sample.ElementEnumerator

train

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

Computes the model with structure graph.

Parameters:

data - the Sample

weights - the weights for the sequences in the Sample

graph - the graph

Throws:

Exception - if something went wrong

drawParameters

public void drawParameters(Sample 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 Sample or null

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

graph - the graph or null for the current graph

Throws:

Exception - if something went wrong

See Also:

DAGModel.drawParameters(Sample, double[]), DAGModel.checkAcyclic(int, int[][])

train

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

Computes the models with structure graph.

Parameters:

models - an array of AbstractModels containing only instances of FSDAGModel

data - the Sample

weights - the weights for the sequences in the Sample

graph - the graph

Throws:

Exception - if something went wrong

set

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

Description copied from class: DiscreteGraphicalModel

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

Overrides:

set in class InhomogeneousDGM

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: InhomogeneousDGM

Returns a String representation of the underlying graph.

Overrides:

getStructure in class DAGModel

Returns:

a String representation of the underlying graph