de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared
Class SharedStructureMixture

java.lang.Object
  de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
      de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
          de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM
              de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.shared.SharedStructureMixture

All Implemented Interfaces:

SequenceScore, StatisticalModel, TrainableStatisticalModel, Storable, Cloneable

public class SharedStructureMixture
extends MixtureTrainSM

This class handles a mixture of models with the same structure that is learned via EM. One well known example is a mixture of trees.

Author:

Jens Keilwagen

Nested Class Summary

Nested classes/interfaces inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

AbstractMixtureTrainSM.Algorithm, AbstractMixtureTrainSM.Parameterization

Field Summary

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

algorithm, algorithmHasBeenRun, alternativeModel, best, burnInTest, componentHyperParams, compProb, counter, dimension, estimateComponentProbs, file, filereader, filewriter, initialIteration, logWeights, model, optimizeModel, sample, samplingIndex, seqWeights, sostream, starts, stationaryIteration, weights

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel

alphabets, length

Constructor Summary

protected	SharedStructureMixture(FSDAGTrainSM[] m, StructureLearner.ModelType model, byte order, int starts, boolean estimateComponentProbs, double[] weights, double alpha, TerminationCondition tc) Creates a new SharedStructureMixture instance with all relevant values.
	SharedStructureMixture(FSDAGTrainSM[] m, StructureLearner.ModelType model, byte order, int starts, double[] weights, double alpha, TerminationCondition tc) Creates a new SharedStructureMixture instance with fixed component weights.
	SharedStructureMixture(FSDAGTrainSM[] m, StructureLearner.ModelType model, byte order, int starts, double alpha, TerminationCondition tc) Creates a new SharedStructureMixture instance which estimates the component probabilities/weights.
	SharedStructureMixture(StringBuffer xml) The standard constructor for the interface Storable.

Method Summary

SharedStructureMixture	clone() Follows the conventions of Object's clone()-method.
protected void	fromXML(StringBuffer representation) This method should only be used by the constructor that works on a StringBuffer.
String	getInstanceName() Should return a short instance name such as iMM(0), BN(2), ...
protected void	getNewParameters(int iteration, double[][] seqWeights, double[] w) This method trains the internal models on the internal data set and the given weights.
String	getStructure() Returns a String representation of the structure of the used models.
StringBuffer	toXML() This method returns an XML representation as StringBuffer of an instance of the implementing class.

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.MixtureTrainSM

doFirstIteration, doFirstIteration, emitDataSetUsingCurrentParameterSet, getLogProbUsingCurrentParameterSetFor, getNewWeights, setTrainData, toString

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM

algorithmHasBeenRun, checkLength, checkModelsForGibbsSampling, continueIterations, continueIterations, createSeqWeightsArray, doFirstIteration, doFirstIteration, draw, emitDataSet, extendSampling, extractFurtherInformation, finalize, getCharacteristics, getFurtherInformation, getIndexOfMaximalComponentFor, getLogPriorTerm, getLogPriorTermForComponentProbs, getLogProbFor, getLogProbFor, getLogScoreFor, getModel, getModels, getMRG, getMRGParams, getNameOfAlgorithm, getNewComponentProbs, getNewParametersForModel, getNumberOfComponents, getNumericalCharacteristics, getScoreForBestRun, getWeights, initModelForSampling, initWithPrior, isInitialized, isInSamplingMode, iterate, iterate, max, modifyWeights, parseNextParameterSet, parseParameterSet, samplingStopped, setAlpha, setOutputStream, setWeights, swap, train

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel

check, getAlphabetContainer, getLength, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getMaximalMarkovOrder, toString, train

Methods inherited from class java.lang.Object

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

Constructor Detail

SharedStructureMixture

public SharedStructureMixture(FSDAGTrainSM[] m,
                              StructureLearner.ModelType model,
                              byte order,
                              int starts,
                              double alpha,
                              TerminationCondition tc)
                       throws IllegalArgumentException,
                              WrongAlphabetException,
                              CloneNotSupportedException

Creates a new SharedStructureMixture instance which estimates the component probabilities/weights.

Parameters:

m - the single models building the mixture model

model - the type of the model

order - the order of the model

starts - the number of times the algorithm will be started in the train-method, at least 1

alpha - the positive parameter for the Dirichlet distribution which is used when you invoke train to initialize the gammas, it is recommended to use alpha = 1 (uniform distribution on a simplex)

tc - the TerminationCondition for stopping the EM-algorithm, tc has to return true from TerminationCondition.isSimple()

Throws:

IllegalArgumentException - if

• the models are not able to score the sequences of same length
• dimension < 1
• weights != null && weights.length != dimension
• weights != null and it exists an i where weights[i] < 0
• starts < 1
• componentHyperParams (hyperparameters for the component assignment prior) are not correct

WrongAlphabetException - if not all models work on the same alphabet

CloneNotSupportedException - if the models can not be cloned

See Also:

StructureLearner.ModelType, SharedStructureMixture(FSDAGTrainSM[], de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner.ModelType, byte, int, boolean, double[], double, TerminationCondition)

SharedStructureMixture

public SharedStructureMixture(FSDAGTrainSM[] m,
                              StructureLearner.ModelType model,
                              byte order,
                              int starts,
                              double[] weights,
                              double alpha,
                              TerminationCondition tc)
                       throws IllegalArgumentException,
                              WrongAlphabetException,
                              CloneNotSupportedException

Creates a new SharedStructureMixture instance with fixed component weights.

Parameters:

m - the single models building the mixture model

model - the type of the model

order - the order of the model

starts - the number of times the algorithm will be started in the train-method, at least 1

weights - null or the weights for the components (then weights.length == models.length)

alpha - the positive parameter for the Dirichlet distribution which is used when you invoke train to initialize the gammas, it is recommended to use alpha = 1 (uniform distribution on a simplex)

tc - the TerminationCondition for stopping the EM-algorithm, tc has to return true from TerminationCondition.isSimple()

Throws:

IllegalArgumentException - if

• the models are not able to score the sequences of same length
• dimension < 1
• weights != null && weights.length != dimension
• weights != null and it exists an i where weights[i] < 0
• starts < 1
• componentHyperParams (hyperparameters for the component assignment prior) are not correct

WrongAlphabetException - if not all models work on the same alphabet

CloneNotSupportedException - if the models can not be cloned

See Also:

StructureLearner.ModelType, SharedStructureMixture(FSDAGTrainSM[], de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner.ModelType, byte, int, boolean, double[], double, TerminationCondition)

SharedStructureMixture

protected SharedStructureMixture(FSDAGTrainSM[] m,
                                 StructureLearner.ModelType model,
                                 byte order,
                                 int starts,
                                 boolean estimateComponentProbs,
                                 double[] weights,
                                 double alpha,
                                 TerminationCondition tc)
                          throws IllegalArgumentException,
                                 WrongAlphabetException,
                                 CloneNotSupportedException

Creates a new SharedStructureMixture instance with all relevant values. This constructor is used by the other main constructors.

Parameters:

m - the single models building the mixture model

model - the type of the model

order - the order of the model

starts - the number of times the algorithm will be started in the train-method, at least 1

estimateComponentProbs - the switch for estimating the component probabilities in the algorithm or to hold them fixed; if the component parameters are fixed, the values of weights will be used, otherwise the componentHyperParams (hyperparameters for the component assignment prior) will be incorporated in the adjustment

weights - null or the weights for the components (then weights.length == models.length)

alpha - the positive parameter for the Dirichlet distribution which is used when you invoke train to initialize the gammas, it is recommended to use alpha = 1 (uniform distribution on a simplex)

tc - the TerminationCondition for stopping the EM-algorithm, tc has to return true from TerminationCondition.isSimple()

Throws:

IllegalArgumentException - if

• the models are not able to score the sequences of same length
• dimension < 1
• weights != null && weights.length != dimension
• weights != null and it exists an i where weights[i] < 0
• starts < 1
• componentHyperParams (hyperparameters for the component assignment prior) are not correct

WrongAlphabetException - if not all models work on the same alphabet

CloneNotSupportedException - if the models can not be cloned

See Also:

StructureLearner.ModelType, MixtureTrainSM.MixtureTrainSM(int, de.jstacs.sequenceScores.statisticalModels.trainable.TrainableStatisticalModel[], int, boolean, double[], double[], de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM.Algorithm, double, TerminationCondition, de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM.Parameterization, int, int, de.jstacs.sampling.BurnInTest)

SharedStructureMixture

public SharedStructureMixture(StringBuffer xml)
                       throws NonParsableException

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

Parameters:

xml - the XML representation as StringBuffer

Throws:

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

See Also:

Storable, MixtureTrainSM.MixtureTrainSM(StringBuffer)

Method Detail

clone

public SharedStructureMixture clone()
                             throws CloneNotSupportedException

Description copied from class: AbstractTrainableStatisticalModel

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

Specified by:

clone in interface SequenceScore

Specified by:

clone in interface TrainableStatisticalModel

Overrides:

clone in class AbstractMixtureTrainSM

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

getStructure

public String getStructure()
                    throws NotTrainedException

Returns a String representation of the structure of the used models.

Returns:

a String representation of the structure of the used models

Throws:

NotTrainedException - if the classifier is not trained yet

See Also:

FSDAGTrainSM.getStructure()

getInstanceName

public String getInstanceName()

Description copied from interface: SequenceScore

Should return a short instance name such as iMM(0), BN(2), ...

Specified by:

getInstanceName in interface SequenceScore

Overrides:

getInstanceName in class AbstractMixtureTrainSM

Returns:

a short instance name

toXML

public StringBuffer toXML()

Description copied from interface: Storable

This method returns an XML representation as StringBuffer of an instance of the implementing class.

Specified by:

toXML in interface Storable

Overrides:

toXML in class AbstractMixtureTrainSM

Returns:

the XML representation

fromXML

protected void fromXML(StringBuffer representation)
                throws NonParsableException

Description copied from class: AbstractTrainableStatisticalModel

This method should only be used by the constructor that works on a StringBuffer. It is the counter part of Storable.toXML().

Overrides:

fromXML in class AbstractMixtureTrainSM

Parameters:

representation - the XML representation of the model

Throws:

NonParsableException - if the StringBuffer is not parsable or the representation is conflicting

See Also:

AbstractTrainableStatisticalModel.AbstractTrainableStatisticalModel(StringBuffer)

getNewParameters

protected void getNewParameters(int iteration,
                                double[][] seqWeights,
                                double[] w)
                         throws Exception

Description copied from class: AbstractMixtureTrainSM

This method trains the internal models on the internal data set and the given weights.

Overrides:

getNewParameters in class AbstractMixtureTrainSM

Parameters:

iteration - the number of times this method has been invoked

seqWeights - the weights for each model and sequence

w - the weights for the components

Throws:

Exception - if the training of the internal models went wrong