de.jstacs.models.discrete.inhomogeneous
Class DAGModel

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

All Implemented Interfaces:

InstantiableFromParameterSet, Model, Storable, Cloneable

Direct Known Subclasses:

BayesianNetworkModel, FSDAGModel

public abstract class DAGModel
extends InhomogeneousDGM

The abstract class for directed acyclic graphical models (DAGModel).

Author:

Jens Keilwagen

Field Summary

protected InhCondProb[]

constraints The constraints for the model.

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

protected	DAGModel(IDGMParameterSet params) This is the main constructor.
protected	DAGModel(StringBuffer xml) The standard constructor for the interface Storable.

Method Summary

protected static boolean	checkAcyclic(int length, int[][] graph) This method checks whether a given graph is acyclic.
DAGModel	clone() Follows the conventions of Object's clone()-method.
protected void	createConstraints(int[][] structure) This method creates the constraints for a given structure.
protected void	drawParameters(Sample data, double[] weights) This method draws the parameter of the model from the likelihood or the posterior, respectively.
Sample	emitSample(int n, int... lengths) This method returns a Sample object containing artificial sequence(s).
protected void	estimateParameters(Sample data, double[] weights) This method estimates the parameter of the model from the likelihood or the posterior, respectively.
protected StringBuffer	getFurtherModelInfos() Returns further model information as a StringBuffer.
double	getLogPriorTerm() Returns a value that is proportional to the log of the prior.
double	getLogProbFor(Sequence sequence, int startpos, int endpos) Returns the logarithm of the probability of (a part of) the given sequence given the model.
NumericalResultSet	getNumericalCharacteristics() Returns the subset of numerical values that are also returned by Model.getCharacteristics().
double	getProbFor(Sequence sequence, int startpos, int endpos) Returns the probability of (a part of) the given sequence given the model.
String	getStructure() Returns a String representation of the underlying graph.
protected void	setFurtherModelInfos(StringBuffer xml) This method replaces the internal model information with those from a StringBuffer.
String	toString() Should give a simple representation (text) of the model as String .

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

check, set, setOutputStream

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

fromXML, getCurrentParameterSet, getDescription, getESS, getXMLTag, isTrained, toXML

Methods inherited from class de.jstacs.models.AbstractModel

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

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.jstacs.models.Model

getInstanceName, train

Field Detail

constraints

protected InhCondProb[] constraints

The constraints for the model.

Constructor Detail

DAGModel

protected DAGModel(IDGMParameterSet params)
            throws CloneNotSupportedException,
                   IllegalArgumentException,
                   NonParsableException

This is the main constructor. It creates a new DAGModel from the given IDGMParameterSet.

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:

InhomogeneousDGM.InhomogeneousDGM(IDGMParameterSet)

DAGModel

protected DAGModel(StringBuffer xml)
            throws NonParsableException

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

Parameters:

xml - the XML representation as StringBuffer

Throws:

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

See Also:

Storable, InhomogeneousDGM.InhomogeneousDGM(StringBuffer)

Method Detail

clone

public DAGModel clone()
               throws CloneNotSupportedException

Description copied from class: AbstractModel

Follows the conventions of Object's clone()-method.

Specified by:

clone in interface Model

Overrides:

clone in class InhomogeneousDGM

Returns:

an object, that is a copy of the current AbstractModel (the member-AlphabetContainer isn't deeply cloned since it is assumed to be immutable). The type of the returned object is defined by the class X directly inherited from AbstractModel. Hence X's clone()-method should work as:
1. Object o = (X)super.clone();
2. all additional member variables of o defined by X that are not of simple data-types like int, double, ... have to be deeply copied
3. return o

Throws:

CloneNotSupportedException - if something went wrong while cloning

emitSample

public Sample emitSample(int n,
                         int... lengths)
                  throws NotTrainedException,
                         Exception

Description copied from interface: Model

This method returns a Sample object containing artificial sequence(s).

There are two different possibilities to create a sample for a model with length 0 (homogeneous models).

1. emitSample( int n, int l ) should return a sample with n sequences of length l.
2. emitSample( int n, int[] l ) should return a sample with n sequences which have a sequence length corresponding to the entry in the given array l.

There are two different possibilities to create a sample for a model with length greater than 0 (inhomogeneous models).
emitSample( int n ) and emitSample( int n, null ) should return a sample with n sequences of length of the model ( Model.getLength()).

The standard implementation throws an Exception.

Specified by:

emitSample in interface Model

Overrides:

emitSample in class AbstractModel

Parameters:

n - the number of sequences that should be contained in the returned sample

lengths - the length of the sequences for a homogeneous model; for an inhomogeneous model this parameter should be null or an array of size 0.

Returns:

a Sample containing the artificial sequence(s)

Throws:

NotTrainedException - if the model is not trained yet

Exception - if the emission did not succeed

See Also:

Sample

getLogPriorTerm

public double getLogPriorTerm()
                       throws Exception

Description copied from interface: Model

Returns a value that is proportional to the log of the prior. For maximum likelihood (ML) 0 should be returned.

Returns:

a value that is proportional to the log of the prior

Throws:

Exception - if something went wrong

See Also:

Model.getPriorTerm()

getLogProbFor

public double getLogProbFor(Sequence sequence,
                            int startpos,
                            int endpos)
                     throws NotTrainedException,
                            Exception

Description copied from interface: Model

Returns the logarithm of the probability of (a part of) the given sequence given the model. If at least one random variable is continuous the value of density function is returned.

For more details see Model.getProbFor(Sequence, int, int)

Specified by:

getLogProbFor in interface Model

Overrides:

getLogProbFor in class AbstractModel

Parameters:

sequence - the given sequence

startpos - the start position within the given sequence

endpos - the last position to be taken into account

Returns:

the logarithm of the probability or the value of the density function of (the part of) the given sequence given the model

Throws:

NotTrainedException - if the model is not trained yet

Exception - if the sequence could not be handled (e.g. startpos > , endpos > sequence.length, ...) by the model

See Also:

Model.getProbFor(Sequence, int, int)

getNumericalCharacteristics

public NumericalResultSet getNumericalCharacteristics()

Description copied from interface: Model

Returns the subset of numerical values that are also returned by Model.getCharacteristics().

Returns:

the numerical characteristics of the current instance of the model

getProbFor

public double getProbFor(Sequence sequence,
                         int startpos,
                         int endpos)
                  throws NotTrainedException,
                         Exception

Description copied from interface: Model

Returns the probability of (a part of) the given sequence given the model. If at least one random variable is continuous the value of density function is returned.

It extends the possibility given by the method Model.getProbFor(Sequence, int) by the fact, that the model could be e.g. homogeneous and therefore the length of the sequences, whose probability should be returned, is not fixed. Additionally the end position of the part of the given sequence is given and the probability of the part from position startpos to endpos (inclusive) should be returned.
The length and the alphabets define the type of data that can be modeled and therefore both has to be checked.

Parameters:

sequence - the given sequence

startpos - the start position within the given sequence

endpos - the last position to be taken into account

Returns:

the probability or the value of the density function of (the part of) the given sequence given the model

Throws:

NotTrainedException - if the model is not trained yet

Exception - if the sequence could not be handled (e.g. startpos > endpos, endpos > sequence.length, ...) by the model

getStructure

public String getStructure()
                    throws NotTrainedException

Description copied from class: InhomogeneousDGM

Returns a String representation of the underlying graph.

Specified by:

getStructure in class InhomogeneousDGM

Returns:

a String representation of the underlying graph

Throws:

NotTrainedException - if the structure is not set, this can only be the case if the model is not trained

toString

public String toString()

Description copied from interface: Model

Should give a simple representation (text) of the model as String .

Specified by:

toString in interface Model

Overrides:

toString in class DiscreteGraphicalModel

Returns:

the representation as String

checkAcyclic

protected static boolean checkAcyclic(int length,
                                      int[][] graph)

This method checks whether a given graph is acyclic.

Parameters:

length - the sequence length (which corresponds to the number of nodes in the graph)

graph - the specified graph

Returns:

true if the given graph is acyclic, false otherwise

createConstraints

protected void createConstraints(int[][] structure)

This method creates the constraints for a given structure.

Parameters:

structure - the specified structure

drawParameters

protected void drawParameters(Sample data,
                              double[] weights)
                       throws Exception

This method draws the parameter of the model from the likelihood or the posterior, respectively.

Parameters:

data - the given data

weights - the weights for the sequences in the data

Throws:

Exception - if something went wrong while counting or drawing

See Also:

ConstraintManager.countInhomogeneous(de.jstacs.data.AlphabetContainer, int, Sample, double[], boolean, de.jstacs.models.discrete.Constraint...), ConstraintManager.drawFreqs(double, InhCondProb...)

estimateParameters

protected void estimateParameters(Sample data,
                                  double[] weights)
                           throws Exception

This method estimates the parameter of the model from the likelihood or the posterior, respectively.

Parameters:

data - the data

weights - the weights for the sequences in the data

Throws:

Exception - if something went wrong while counting or estimating

See Also:

drawParameters(Sample, double[])

getFurtherModelInfos

protected StringBuffer getFurtherModelInfos()

Description copied from class: DiscreteGraphicalModel

Returns further model information as a StringBuffer.

Specified by:

getFurtherModelInfos in class DiscreteGraphicalModel

Returns:

further model information like parameters of the distribution etc. in XML format

See Also:

DiscreteGraphicalModel.toXML()

setFurtherModelInfos

protected void setFurtherModelInfos(StringBuffer xml)
                             throws NonParsableException

Description copied from class: DiscreteGraphicalModel

This method replaces the internal model information with those from a StringBuffer.

Specified by:

setFurtherModelInfos in class DiscreteGraphicalModel

Parameters:

xml - contains the model information like parameters of the distribution etc. in XML format

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

DiscreteGraphicalModel.fromXML(StringBuffer)