de.jstacs.models.mixture.gibbssampling
Class FSDAGModelForGibbsSampling

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
                      de.jstacs.models.mixture.gibbssampling.FSDAGModelForGibbsSampling

All Implemented Interfaces:

InstantiableFromParameterSet, GibbsSamplingComponent, Model, Storable, Cloneable

public class FSDAGModelForGibbsSampling
extends FSDAGModel
implements GibbsSamplingComponent

This is the class for a fixed structure directed acyclic graphical model that can be used in a Gibbs sampling.

Author:

Berit Haldemann, Jens Keilwagen

See Also:

AbstractMixtureModel

Field Summary

protected int[]	counter The counter for the sampling steps of each sampling
protected File[]	paramsFile The files for saving the parameters while the sampling.
protected BufferedReader	reader The reader for the paramsFile after a sampling.
protected int	samplingIndex The index of the current sampling.
protected BufferedWriter	writer The writer for the paramsFile in a sampling.

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

FSDAGModelForGibbsSampling(FSDAGMParameterSet params)
The default constructor.

FSDAGModelForGibbsSampling(StringBuffer representation)
This is the constructor for Storable.

Method Summary

FSDAGModelForGibbsSampling	clone() In this method the reader is set to null
void	drawParameters(Sample data, double[] weights) This method draws the parameter of the model from the likelihood respectively posterior.
void	drawParameters(Sample data, double[] weights, int[][] graph) This method draws the parameters of the model from the a posteriori density.
void	extendSampling(int sampling, boolean extend) This method allows to extend a sampling.
protected void	finalize()
protected StringBuffer	getFurtherModelInfos()
void	initModelForSampling(int starts) This method initializes the model for the sampling.
boolean	isInSamplingMode() This method returns true if the object is currently used in a sampling, otherwise false.
boolean	parseNextParameterSet() This method allows the user to parse the next set of parameters (from a file).
boolean	parseParameterSet(int sampling, int n) This method allows the user to parse the set of parameters with index n of a certain sampling (from a file).
void	samplingStopped() This method is the opposite of the method GibbsSamplingComponent.extendSampling(int, boolean).
protected void	setFurtherModelInfos(StringBuffer xml) In this method the reader is set to null
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.FSDAGModel

getInstanceName, getMaximalMarkovOrder, getStructure, getXMLTag, set, train

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

checkAcyclic, createConstraints, emitSample, estimateParameters, getLogPriorTerm, getLogProbFor, getNumericalCharacteristics, getProbFor, 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, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

paramsFile

protected File[] paramsFile

The files for saving the parameters while the sampling.

counter

protected int[] counter

The counter for the sampling steps of each sampling

samplingIndex

protected int samplingIndex

The index of the current sampling.

writer

protected BufferedWriter writer

The writer for the paramsFile in a sampling.

reader

protected BufferedReader reader

The reader for the paramsFile after a sampling.

Constructor Detail

FSDAGModelForGibbsSampling

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

The default constructor.

Parameters:

params - the 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

FSDAGModelForGibbsSampling

public FSDAGModelForGibbsSampling(StringBuffer representation)
                           throws NonParsableException

This is the constructor for Storable.

Parameters:

representation - the xml representation

Throws:

NonParsableException - if the representation could not be parsed.

Method Detail

clone

public FSDAGModelForGibbsSampling clone()
                                 throws CloneNotSupportedException

In this method the reader is set to null

Specified by:

clone in interface Model

Overrides:

clone in class DAGModel

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

parseNextParameterSet

public boolean parseNextParameterSet()

Description copied from interface: GibbsSamplingComponent

This method allows the user to parse the next set of parameters (from a file).

Specified by:

parseNextParameterSet in interface GibbsSamplingComponent

Returns:

true if the parameters could be parsed, otherwise false

See Also:

GibbsSamplingComponent.parseParameterSet(int, int)

parseParameterSet

public boolean parseParameterSet(int sampling,
                                 int n)
                          throws NumberFormatException,
                                 IOException

Description copied from interface: GibbsSamplingComponent

This method allows the user to parse the set of parameters with index n of a certain sampling (from a file). The internal numbering should start with 0. The parameter set with index 0 is the initial (random) parameter set. It is recommended that a series of parameter sets is accessed by the following lines:

for( sampling = 0; sampling < numSampling; sampling++ )
{


boolean b = parseParameterSet( sampling, n );
while( b )
{
//do something
b = parseNextParameterSet();
}
}


Specified by:

parseParameterSet in interface GibbsSamplingComponent

Parameters:

sampling - the index of the sampling

n - the index of the parameter set

Returns:

true if the parameter set could be parsed

Throws:

NumberFormatException

IOException

See Also:

GibbsSamplingComponent.parseNextParameterSet()

initModelForSampling

public void initModelForSampling(int starts)
                          throws IOException

Description copied from interface: GibbsSamplingComponent

This method initializes the model for the sampling. For instance this method can be used to create new files where all parameter sets are stored.

Specified by:

initModelForSampling in interface GibbsSamplingComponent

Parameters:

starts - the number of different sampling starts that will be done

Throws:

IOException - if something went wrong

See Also:

File.createTempFile(String, String, java.io.File )

extendSampling

public void extendSampling(int sampling,
                           boolean extend)
                    throws IOException

Description copied from interface: GibbsSamplingComponent

This method allows to extend a sampling.

Specified by:

extendSampling in interface GibbsSamplingComponent

Parameters:

sampling - the index of the sampling

extend - whether to append the sampled parameters to an existing file or to overwrite the file

Throws:

IOException - if the file could not be handled correctly

drawParameters

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

Description copied from class: DAGModel

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

Specified by:

drawParameters in interface GibbsSamplingComponent

Overrides:

drawParameters in class DAGModel

Parameters:

data - the data

weights - the weights for the sequences in the data

Throws:

Exception - if something went wrong while counting or drawing

See Also:

GibbsSamplingComponent.initModelForSampling(int), GibbsSamplingComponent.parseParameterSet(int, int), GibbsSamplingComponent.parseNextParameterSet()

samplingStopped

public void samplingStopped()
                     throws IOException

Description copied from interface: GibbsSamplingComponent

This method is the opposite of the method GibbsSamplingComponent.extendSampling(int, boolean). It can be used for closing any streams of writer, ...

Specified by:

samplingStopped in interface GibbsSamplingComponent

Throws:

IOException - if something went wrong

See Also:

GibbsSamplingComponent.extendSampling(int, boolean)

getFurtherModelInfos

protected StringBuffer getFurtherModelInfos()

Overrides:

getFurtherModelInfos in class DAGModel

Returns:

the model infos like parameters of the distibution ... in xml format

See Also:

DiscreteGraphicalModel.toXML()

setFurtherModelInfos

protected void setFurtherModelInfos(StringBuffer xml)
                             throws NonParsableException

In this method the reader is set to null

Overrides:

setFurtherModelInfos in class DAGModel

Parameters:

xml - contains the model infos like parameters of the distibution ... in xml format

Throws:

NonParsableException - if the StringBuffer could not be parsed

See Also:

DiscreteGraphicalModel.fromXML(StringBuffer)

isInSamplingMode

public boolean isInSamplingMode()

Description copied from interface: GibbsSamplingComponent

This method returns true if the object is currently used in a sampling, otherwise false.

Specified by:

isInSamplingMode in interface GibbsSamplingComponent

Returns:

true if the object is currently used in a sampling

drawParameters

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

Description copied from class: FSDAGModel

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.

Overrides:

drawParameters in class FSDAGModel

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

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.

Specified by:

train in interface Model

Overrides:

train in class FSDAGModel

Parameters:

data - the given sequences

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

Throws:

Exception - an Exception should be thrown if the training did not succeed (e.g. the weights dimension of weights and number of samples does not match).

See Also:

Sample.getElementAt(int), Sample.ElementEnumerator

train

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

Description copied from class: FSDAGModel

Computes the model with structure graph

Overrides:

train in class FSDAGModel

Parameters:

data - the sample

weights - the weights for the sequences in the sample

graph - the graph

Throws:

Exception - if something went wrong

finalize

protected void finalize()
                 throws Throwable

Overrides:

finalize in class Object

Throws:

Throwable