de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif
Class HiddenMotifMixture

java.lang.Object
  de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
      de.jstacs.sequenceScores.statisticalModels.trainable.mixture.AbstractMixtureTrainSM
          de.jstacs.sequenceScores.statisticalModels.trainable.mixture.motif.HiddenMotifMixture

All Implemented Interfaces:

MotifDiscoverer, SequenceScore, StatisticalModel, TrainableStatisticalModel, Storable, Cloneable

Direct Known Subclasses:

ZOOPSTrainSM

public abstract class HiddenMotifMixture
extends AbstractMixtureTrainSM
implements MotifDiscoverer

This is the main class that every generative motif discoverer should implement.

Author:

Jens Keilwagen

Nested Class Summary

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

AbstractMixtureTrainSM.Algorithm, AbstractMixtureTrainSM.Parameterization

Nested classes/interfaces inherited from interface de.jstacs.motifDiscovery.MotifDiscoverer

MotifDiscoverer.KindOfProfile

Field Summary

protected PositionPrior

posPrior The prior for the positions.

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

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

protected

HiddenMotifMixture(TrainableStatisticalModel[] models, boolean[] optimzeArray, int components, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double[] weights, PositionPrior posPrior, AbstractMixtureTrainSM.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureTrainSM.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest) Creates a new HiddenMotifMixture.

Method Summary

protected void	checkLength(int index, int l) This method checks if the length l of the model with index index is capable for the current instance.
HiddenMotifMixture	clone() Follows the conventions of Object's clone()-method.
protected Sequence[]	emitDataSetUsingCurrentParameterSet(int n, int... lengths) Standard implementation throwing an OperationNotSupportedException.
protected void	extractFurtherInformation(StringBuffer xml) This method is used in the subclasses to extract further information from the XML representation and to set these as values of the instance.
protected StringBuffer	getFurtherInformation() This method is used in the subclasses to append further information to the XML representation.
String	getInstanceName() Should return a short instance name such as iMM(0), BN(2), ...
abstract int	getMinimalSequenceLength() Returns the minimal length a sequence respectively a sample has to have.
protected void	getNewParameters(int iteration, double[][] seqWeights, double[] w) This method trains the internal models on the internal sample and the given weights.
String	toString() Should give a simple representation (text) of the model as String.
void	train(DataSet data, double[] weights) Trains the TrainableStatisticalModel object given the data as DataSet using the specified weights.
abstract void	trainBgModel(DataSet data, double[] weights) This method trains the background model.

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

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

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

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

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.jstacs.motifDiscovery.MotifDiscoverer

getGlobalIndexOfMotifInComponent, getIndexOfMaximalComponentFor, getMotifLength, getNumberOfComponents, getNumberOfMotifs, getNumberOfMotifsInComponent, getProfileOfScoresFor, getStrandProbabilitiesFor

Methods inherited from interface de.jstacs.Storable

toXML

Field Detail

posPrior

protected PositionPrior posPrior

The prior for the positions.

Constructor Detail

HiddenMotifMixture

protected HiddenMotifMixture(TrainableStatisticalModel[] models,
                             boolean[] optimzeArray,
                             int components,
                             int starts,
                             boolean estimateComponentProbs,
                             double[] componentHyperParams,
                             double[] weights,
                             PositionPrior posPrior,
                             AbstractMixtureTrainSM.Algorithm algorithm,
                             double alpha,
                             TerminationCondition tc,
                             AbstractMixtureTrainSM.Parameterization parametrization,
                             int initialIteration,
                             int stationaryIteration,
                             BurnInTest burnInTest)
                      throws CloneNotSupportedException,
                             IllegalArgumentException,
                             WrongAlphabetException

Creates a new HiddenMotifMixture. This constructor can be used for any algorithm since it takes all necessary values as parameters.

Parameters:

models - the single models building the HiddenMotifMixture, if the model is trained using AbstractMixtureTrainSM.Algorithm.GIBBS_SAMPLING the models that will be adjusted have to implement SamplingComponent. The models that are used for the flanking sequences have to be able to score sequences of arbitrary length.

optimzeArray - a array of switches whether to train or not the corresponding model

components - the number of components (e.g. for ZOOPS this is 2)

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 will be incorporated in the adjustment

componentHyperParams - the hyperparameters for the component assignment prior

• will only be used if estimateComponentProbs == true
• the array has to be null or has to have length dimension
• null or an array with all values zero (0) then ML
• otherwise (all values positive) a prior is used (MAP, MP, ...)
• depends on the parameterization

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

posPrior - this object determines the positional distribution that shall be used

algorithm - either AbstractMixtureTrainSM.Algorithm.EM or AbstractMixtureTrainSM.Algorithm.GIBBS_SAMPLING

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

parametrization - only for AbstractMixtureTrainSM.Algorithm.EM
the type of the component probability parameterization;

• AbstractMixtureTrainSM.Parameterization.THETA or AbstractMixtureTrainSM.Parameterization.LAMBDA
• the parameterization of a component is determined by the component model
• it is recommended to use the same parameterization for the components and the component assignment probabilities
• it is recommended to use AbstractMixtureTrainSM.Parameterization.LAMBDA

initialIteration - only for AbstractMixtureTrainSM.Algorithm.GIBBS_SAMPLING
the positive length of the initial sampling phase (at least 1, at most stationaryIteration/starts)

stationaryIteration - only for AbstractMixtureTrainSM.Algorithm.GIBBS_SAMPLING
the positive length of the stationary phase (at least 1) (summed over all starts), i.e. the number of parameter sets that is used in approximation

burnInTest - only for AbstractMixtureTrainSM.Algorithm.GIBBS_SAMPLING
the test that will be used to determine the length of the burn-in phase

Throws:

IllegalArgumentException - if

• the models are not able to score the sequence of the corresponding length
• weights != null && weights.length != 2
• weights != null and it exists an i where weights[i] < 0
• starts < 1
• componentHyperParams are not correct
• the algorithm specific parameters are not correct

WrongAlphabetException - if not all models work on the same simple alphabet

CloneNotSupportedException - if the models can not be cloned

HiddenMotifMixture

protected HiddenMotifMixture(StringBuffer xml)
                      throws NonParsableException

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

Parameters:

xml - the XML representation of the model as StringBuffer

Throws:

NonParsableException - if the StringBuffer can not be parsed

Method Detail

clone

public HiddenMotifMixture clone()
                         throws CloneNotSupportedException

Description copied from class: AbstractTrainableStatisticalModel

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

Specified by:

clone in interface MotifDiscoverer

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

See Also:

Cloneable

getFurtherInformation

protected StringBuffer getFurtherInformation()

Description copied from class: AbstractMixtureTrainSM

This method is used in the subclasses to append further information to the XML representation.

Overrides:

getFurtherInformation in class AbstractMixtureTrainSM

Returns:

a part of the XML representation

See Also:

AbstractMixtureTrainSM.extractFurtherInformation(StringBuffer)

extractFurtherInformation

protected void extractFurtherInformation(StringBuffer xml)
                                  throws NonParsableException

Description copied from class: AbstractMixtureTrainSM

This method is used in the subclasses to extract further information from the XML representation and to set these as values of the instance.

Overrides:

extractFurtherInformation in class AbstractMixtureTrainSM

Parameters:

xml - the XML representation

Throws:

NonParsableException - if the XML representation is not parsable

See Also:

AbstractMixtureTrainSM.getFurtherInformation()

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 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 TrainableStatisticalModel

Overrides:

train in class AbstractMixtureTrainSM

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 sample do not match)

See Also:

DataSet.getElementAt(int), DataSet.ElementEnumerator

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 sample 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

trainBgModel

public abstract void trainBgModel(DataSet data,
                                  double[] weights)
                           throws Exception

This method trains the background model. This can be useful if the background model is not trained during the EM-algorithm.

Parameters:

data - the sample

weights - the weights

Throws:

Exception - if something went wrong

checkLength

protected void checkLength(int index,
                           int l)

Description copied from class: AbstractMixtureTrainSM

This method checks if the length l of the model with index index is capable for the current instance. Otherwise an IllegalArgumentException is thrown.

Overrides:

checkLength in class AbstractMixtureTrainSM

Parameters:

index - the index of the model

l - the length of the model

getMinimalSequenceLength

public abstract int getMinimalSequenceLength()

Returns the minimal length a sequence respectively a sample has to have.

Returns:

the minimal length a sequence respectively a sample has to have

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

toString

public String toString()

Description copied from interface: TrainableStatisticalModel

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

Specified by:

toString in interface TrainableStatisticalModel

Overrides:

toString in class Object

Returns:

the representation as String

emitDataSetUsingCurrentParameterSet

protected Sequence[] emitDataSetUsingCurrentParameterSet(int n,
                                                         int... lengths)
                                                  throws Exception

Standard implementation throwing an OperationNotSupportedException.

Specified by:

emitDataSetUsingCurrentParameterSet in class AbstractMixtureTrainSM

Parameters:

n - the number of sequences to be sampled

lengths - the corresponding lengths

Returns:

an array of sequences

Throws:

Exception - if it was impossible to sample the sequences

See Also:

StatisticalModel.emitDataSet(int, int...)