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

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
                      de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.FSDAGModelForGibbsSampling

All Implemented Interfaces:

InstantiableFromParameterSet, GibbsSamplingModel, SamplingComponent, SequenceScore, StatisticalModel, TrainableStatisticalModel, Storable, Cloneable

public class FSDAGModelForGibbsSampling
extends FSDAGTrainSM
implements GibbsSamplingModel

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

Author:

Berit Haldemann, Jens Keilwagen

See Also:

AbstractMixtureTrainSM

Field Summary

protected int[]	counter The counter for the sampling steps of each sampling.
protected File[]	paramsFile The files for saving the parameters during 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.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

FSDAGModelForGibbsSampling(FSDAGModelForGibbsSamplingParameterSet params)
The default constructor.

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

Method Summary

void	acceptParameters() This methods accepts the drawn parameters.
FSDAGModelForGibbsSampling	clone() In this method the reader is set to null and the paramsFile is cloned.
void	drawParameters(DataSet data, double[] weights) This method draws the parameter of the model from the likelihood or the posterior, respectively.
void	drawParameters(DataSet 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() Returns further model information as a StringBuffer.
void	initForSampling(int starts) This method initializes the instance 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 SamplingComponent.extendSampling(int, boolean).
protected void	setFurtherModelInfos(StringBuffer xml) This method replaces the internal model information with those from a StringBuffer.
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.

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

getInstanceName, getMaximalMarkovOrder, getStructure, getXMLTag, set, train

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

checkAcyclic, createConstraints, emitDataSet, estimateParameters, getLogPriorTerm, getLogProbFor, getNumericalCharacteristics, 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, toString, 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 during 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(FSDAGModelForGibbsSamplingParameterSet 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 the Storable interface. Creates a new FSDAGModelForGibbsSampling out of its XML representation.

Parameters:

representation - the XML representation as StringBuffer

Throws:

NonParsableException - if the StringBuffer could not be parsed.

Method Detail

clone

public FSDAGModelForGibbsSampling clone()
                                 throws CloneNotSupportedException

In this method the reader is set to null and the paramsFile is cloned.

Specified by:

clone in interface SequenceScore

Specified by:

clone in interface TrainableStatisticalModel

Overrides:

clone in class DAGTrainSM

Returns:

an object, that is a copy of the current AbstractTrainableStatisticalModel (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 AbstractTrainableStatisticalModel. 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

See Also:

DAGTrainSM.clone(), Object.clone()

parseNextParameterSet

public boolean parseNextParameterSet()

Description copied from interface: SamplingComponent

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

Specified by:

parseNextParameterSet in interface SamplingComponent

Returns:

true if the parameters could be parsed, otherwise false

See Also:

SamplingComponent.parseParameterSet(int, int)

parseParameterSet

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

Description copied from interface: SamplingComponent

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 SamplingComponent

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:

SamplingComponent.parseNextParameterSet()

initForSampling

public void initForSampling(int starts)
                     throws IOException

Description copied from interface: SamplingComponent

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

Specified by:

initForSampling in interface SamplingComponent

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: SamplingComponent

This method allows to extend a sampling.

Specified by:

extendSampling in interface SamplingComponent

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(DataSet data,
                           double[] weights)
                    throws Exception

Description copied from class: DAGTrainSM

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

Specified by:

drawParameters in interface GibbsSamplingModel

Overrides:

drawParameters in class DAGTrainSM

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, DataSet, double[], boolean, de.jstacs.sequenceScores.statisticalModels.trainable.discrete.Constraint...), ConstraintManager.drawFreqs(double, InhCondProb...)

samplingStopped

public void samplingStopped()
                     throws IOException

Description copied from interface: SamplingComponent

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

Specified by:

samplingStopped in interface SamplingComponent

Throws:

IOException - if something went wrong

See Also:

SamplingComponent.extendSampling(int, boolean)

getFurtherModelInfos

protected StringBuffer getFurtherModelInfos()

Description copied from class: DiscreteGraphicalTrainSM

Returns further model information as a StringBuffer.

Overrides:

getFurtherModelInfos in class DAGTrainSM

Returns:

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

See Also:

DiscreteGraphicalTrainSM.toXML()

setFurtherModelInfos

protected void setFurtherModelInfos(StringBuffer xml)
                             throws NonParsableException

Description copied from class: DiscreteGraphicalTrainSM

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

Overrides:

setFurtherModelInfos in class DAGTrainSM

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:

DiscreteGraphicalTrainSM.fromXML(StringBuffer)

isInSamplingMode

public boolean isInSamplingMode()

Description copied from interface: SamplingComponent

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

Specified by:

isInSamplingMode in interface SamplingComponent

Returns:

true if the object is currently used in a sampling, otherwise false

drawParameters

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

Description copied from class: FSDAGTrainSM

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 FSDAGTrainSM

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 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.

Specified by:

train in interface TrainableStatisticalModel

Overrides:

train in class FSDAGTrainSM

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

train

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

Description copied from class: FSDAGTrainSM

Computes the model with structure graph.

Overrides:

train in class FSDAGTrainSM

Parameters:

data - the DataSet

weights - the weights for the sequences in the DataSet

graph - the graph

Throws:

Exception - if something went wrong

finalize

protected void finalize()
                 throws Throwable

Overrides:

finalize in class Object

Throws:

Throwable

acceptParameters

public void acceptParameters()
                      throws IOException

Description copied from interface: SamplingComponent

This methods accepts the drawn parameters. Internally the drawn parameters should be saved (to a file).

Specified by:

acceptParameters in interface SamplingComponent

Throws:

IOException - if the file could not be handled correctly