de.jstacs.models.mixture
Class StrandModel

java.lang.Object
  de.jstacs.models.AbstractModel
      de.jstacs.models.mixture.AbstractMixtureModel
          de.jstacs.models.mixture.StrandModel

All Implemented Interfaces:

Model, Storable, Cloneable

public class StrandModel
extends AbstractMixtureModel

This model handles sequences that can either lie on the forward strand or on the reverse complementary strand. Therefore it is recommended to use this model only for DNA, but it is not restricted to DNA.

If you use Gibbs Sampling temporary files will be created in the Java temp folder. These files will be deleted if no reference to the current instance exists and the Garbage Collector is called. Therefore it is recommended to call the Garbage Collector explicitly at the end of any application.

Author:

Jens Keilwagen

See Also:

Model

Nested Class Summary

Nested classes/interfaces inherited from class de.jstacs.models.mixture.AbstractMixtureModel

AbstractMixtureModel.Algorithm, AbstractMixtureModel.Parameterization

Field Summary

Fields inherited from class de.jstacs.models.mixture.AbstractMixtureModel

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.models.AbstractModel

alphabets, length

Constructor Summary

protected	StrandModel(Model model, int starts, boolean estimateComponentProbs, double[] componentHyperParams, double forwardStrandProb, AbstractMixtureModel.Algorithm algorithm, double alpha, TerminationCondition tc, AbstractMixtureModel.Parameterization parametrization, int initialIteration, int stationaryIteration, BurnInTest burnInTest) Creates a new StrandModel.
	StrandModel(Model model, int starts, double[] componentHyperParams, double alpha, TerminationCondition tc, AbstractMixtureModel.Parameterization parametrization) Creates an instance using EM and estimating the component probabilities.
	StrandModel(Model model, int starts, double[] componentHyperParams, int initialIteration, int stationaryIteration, BurnInTest burnInTest) Creates an instance using Gibbs Sampling and sampling the component probabilities.
	StrandModel(Model model, int starts, double forwardStrandProb, double alpha, TerminationCondition tc, AbstractMixtureModel.Parameterization parametrization) Creates an instance using EM and fixed component probabilities.
	StrandModel(Model model, int starts, double forwardStrandProb, int initialIteration, int stationaryIteration, BurnInTest burnInTest) Creates an instance using Gibbs Sampling and fixed component probabilities.
	StrandModel(StringBuffer stringBuff) The constructor for the interface Storable.

Method Summary

protected double[][]	doFirstIteration(double[] dataWeights, MultivariateRandomGenerator m, MRGParams[] params) This method will do the first step in the train algorithm for the current model on the internal sample.
protected Sequence[]	emitSampleUsingCurrentParameterSet(int n, int... lengths) The method returns an array of sequences using the current parameter set.
protected double	getLogProbUsingCurrentParameterSetFor(int component, Sequence s, int start, int end) Returns the logarithmic probability for the sequence and the given component using the current parameter set.
protected double	getNewWeights(double[] dataWeights, double[] w, double[][] seqweights) Computes sequence weights and returns the score.
void	setTrainData(Sample s) This method is invoked by the train-method and sets for a given sample the sample that should be used for train.
String	toString() Should give a simple representation (text) of the model as String .

Methods inherited from class de.jstacs.models.mixture.AbstractMixtureModel

algorithmHasBeenRun, checkLength, checkModelsForGibbsSampling, clone, continueIterations, continueIterations, createSeqWeightsArray, doFirstIteration, doFirstIteration, draw, emitSample, extendSampling, extractFurtherInformation, finalize, fromXML, getCharacteristics, getFurtherInformation, getIndexOfMaximalComponentFor, getInstanceName, getLogPriorTerm, getLogPriorTermForComponentProbs, getLogProbFor, getLogProbFor, getLogProbFor, getModel, getModels, getMRG, getMRGParams, getNameOfAlgorithm, getNewComponentProbs, getNewParameters, getNewParametersForModel, getNumberOfComponents, getNumericalCharacteristics, getProbFor, getScoreForBestRun, getTime, getWeights, initModelForSampling, initWithPrior, isInSamplingMode, isTrained, iterate, iterate, max, modifyWeights, parseNextParameterSet, parseParameterSet, samplingStopped, set, setAlpha, setOutputStream, setWeights, swap, toXML, train

Methods inherited from class de.jstacs.models.AbstractModel

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

Methods inherited from class java.lang.Object

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

Constructor Detail

StrandModel

protected StrandModel(Model model,
                      int starts,
                      boolean estimateComponentProbs,
                      double[] componentHyperParams,
                      double forwardStrandProb,
                      AbstractMixtureModel.Algorithm algorithm,
                      double alpha,
                      TerminationCondition tc,
                      AbstractMixtureModel.Parameterization parametrization,
                      int initialIteration,
                      int stationaryIteration,
                      BurnInTest burnInTest)
               throws CloneNotSupportedException,
                      IllegalArgumentException,
                      WrongAlphabetException

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

Parameters:

model - the model building the basis of the StrandModel, if the instance is trained using AbstractMixtureModel.Algorithm.GIBBS_SAMPLING the model has to implement SamplingComponent

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 value forwardStrandProb 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 2
• 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

forwardStrandProb - the probability for the forward strand

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

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

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

• AbstractMixtureModel.Parameterization.THETA or AbstractMixtureModel.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 AbstractMixtureModel.Parameterization.LAMBDA

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

stationaryIteration - only for AbstractMixtureModel.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 for approximation

burnInTest - only for AbstractMixtureModel.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 length length
• dimension < 1
• weights != null && weights.length != dimension
• 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 alphabet

CloneNotSupportedException - if the models can not be cloned

StrandModel

public StrandModel(Model model,
                   int starts,
                   double[] componentHyperParams,
                   double alpha,
                   TerminationCondition tc,
                   AbstractMixtureModel.Parameterization parametrization)
            throws CloneNotSupportedException,
                   IllegalArgumentException,
                   WrongAlphabetException

Creates an instance using EM and estimating the component probabilities.

Parameters:

model - the model building the basis of the StrandModel, if the instance is trained using AbstractMixtureModel.Algorithm.GIBBS_SAMPLING the model has to implement SamplingComponent

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

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 2
• 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

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

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

• AbstractMixtureModel.Parameterization.THETA or AbstractMixtureModel.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 AbstractMixtureModel.Parameterization.LAMBDA

Throws:

IllegalArgumentException - if

• the models are not able to score the sequence of length length
• dimension < 1
• weights != null && weights.length != dimension
• 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 alphabet

CloneNotSupportedException - if the models can not be cloned

See Also:

StrandModel(de.jstacs.models.Model, int, boolean, double[], double, de.jstacs.models.mixture.AbstractMixtureModel.Algorithm, double, TerminationCondition, de.jstacs.models.mixture.AbstractMixtureModel.Parameterization, int, int, de.jstacs.sampling.BurnInTest ), AbstractMixtureModel.Algorithm.EM

StrandModel

public StrandModel(Model model,
                   int starts,
                   double forwardStrandProb,
                   double alpha,
                   TerminationCondition tc,
                   AbstractMixtureModel.Parameterization parametrization)
            throws CloneNotSupportedException,
                   IllegalArgumentException,
                   WrongAlphabetException

Creates an instance using EM and fixed component probabilities.

Parameters:

model - the model building the basis of the StrandModel, if the instance is trained using AbstractMixtureModel.Algorithm.GIBBS_SAMPLING the model has to implement SamplingComponent

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

forwardStrandProb - the probability for the forward strand

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

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

• AbstractMixtureModel.Parameterization.THETA or AbstractMixtureModel.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 AbstractMixtureModel.Parameterization.LAMBDA

Throws:

IllegalArgumentException - if

• the models are not able to score the sequence of length length
• dimension < 1
• weights != null && weights.length != dimension
• 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 alphabet

CloneNotSupportedException - if the models can not be cloned

See Also:

StrandModel(de.jstacs.models.Model, int, boolean, double[], double, de.jstacs.models.mixture.AbstractMixtureModel.Algorithm, double, TerminationCondition, de.jstacs.models.mixture.AbstractMixtureModel.Parameterization, int, int, de.jstacs.sampling.BurnInTest ), AbstractMixtureModel.Algorithm.EM

StrandModel

public StrandModel(Model model,
                   int starts,
                   double[] componentHyperParams,
                   int initialIteration,
                   int stationaryIteration,
                   BurnInTest burnInTest)
            throws CloneNotSupportedException,
                   IllegalArgumentException,
                   WrongAlphabetException

Creates an instance using Gibbs Sampling and sampling the component probabilities.

Parameters:

model - the model building the basis of the StrandModel, if the instance is trained using AbstractMixtureModel.Algorithm.GIBBS_SAMPLING the model has to implement SamplingComponent

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

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 2
• 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

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

stationaryIteration - only for AbstractMixtureModel.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 for approximation

burnInTest - only for AbstractMixtureModel.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 length length
• dimension < 1
• weights != null && weights.length != dimension
• 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 alphabet

CloneNotSupportedException - if the models can not be cloned

See Also:

StrandModel(de.jstacs.models.Model, int, boolean, double[], double, de.jstacs.models.mixture.AbstractMixtureModel.Algorithm, double, TerminationCondition, de.jstacs.models.mixture.AbstractMixtureModel.Parameterization, int, int, de.jstacs.sampling.BurnInTest ), AbstractMixtureModel.Algorithm.GIBBS_SAMPLING

StrandModel

public StrandModel(Model model,
                   int starts,
                   double forwardStrandProb,
                   int initialIteration,
                   int stationaryIteration,
                   BurnInTest burnInTest)
            throws CloneNotSupportedException,
                   IllegalArgumentException,
                   WrongAlphabetException

Creates an instance using Gibbs Sampling and fixed component probabilities.

Parameters:

model - the model building the basis of the StrandModel, if the instance is trained using AbstractMixtureModel.Algorithm.GIBBS_SAMPLING the model has to implement SamplingComponent

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

forwardStrandProb - the probability for the forward strand

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

stationaryIteration - only for AbstractMixtureModel.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 for approximation

burnInTest - only for AbstractMixtureModel.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 length length
• dimension < 1
• weights != null && weights.length != dimension
• 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 alphabet

CloneNotSupportedException - if the models can not be cloned

See Also:

StrandModel(de.jstacs.models.Model, int, boolean, double[], double, de.jstacs.models.mixture.AbstractMixtureModel.Algorithm, double, TerminationCondition, de.jstacs.models.mixture.AbstractMixtureModel.Parameterization, int, int, de.jstacs.sampling.BurnInTest ), AbstractMixtureModel.Algorithm.GIBBS_SAMPLING

StrandModel

public StrandModel(StringBuffer stringBuff)
            throws NonParsableException

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

Parameters:

stringBuff - the StringBuffer containing the XML representation of the model

Throws:

NonParsableException - if the StringBuffer could not be parsed

Method Detail

setTrainData

public void setTrainData(Sample s)
                  throws Exception

Description copied from class: AbstractMixtureModel

This method is invoked by the train-method and sets for a given sample the sample that should be used for train.

Specified by:

setTrainData in class AbstractMixtureModel

Parameters:

s - the given sample of sequences

Throws:

Exception - if something went wrong

doFirstIteration

protected double[][] doFirstIteration(double[] dataWeights,
                                      MultivariateRandomGenerator m,
                                      MRGParams[] params)
                               throws Exception

Description copied from class: AbstractMixtureModel

This method will do the first step in the train algorithm for the current model on the internal sample. The initialization will be done by randomly setting the component membership. This is useful when nothing is known about the problem.

Specified by:

doFirstIteration in class AbstractMixtureModel

Parameters:

dataWeights - null or the weights of each element of the sample

m - the multivariate random generator

params - the parameters for the multivariate random generator

Returns:

the weighting array used to initialize, this array can be reused in the following iterations

Throws:

Exception - if something went wrong

getNewWeights

protected double getNewWeights(double[] dataWeights,
                               double[] w,
                               double[][] seqweights)
                        throws Exception

Computes sequence weights and returns the score.

Specified by:

getNewWeights in class AbstractMixtureModel

Parameters:

dataWeights - the weights for the internal sample (should not be changed)

w - the array for the statistic of the component parameters (shall be filled)

seqweights - an array containing for each component the weights for each sequence (shall be filled)

Returns:

the score

Throws:

Exception - if something went wrong

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 Object

Returns:

the representation as String

emitSampleUsingCurrentParameterSet

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

Description copied from class: AbstractMixtureModel

The method returns an array of sequences using the current parameter set.

Specified by:

emitSampleUsingCurrentParameterSet in class AbstractMixtureModel

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

NotTrainedException

See Also:

AbstractModel.emitSample(int, int...)

getLogProbUsingCurrentParameterSetFor

protected double getLogProbUsingCurrentParameterSetFor(int component,
                                                       Sequence s,
                                                       int start,
                                                       int end)
                                                throws Exception

Description copied from class: AbstractMixtureModel

Returns the logarithmic probability for the sequence and the given component using the current parameter set.

Specified by:

getLogProbUsingCurrentParameterSetFor in class AbstractMixtureModel

Parameters:

component - the index of the component

s - the sequence

start - the start position in the sequence

end - the end position in the sequence

Returns:

log P(s,component) = log P(s|component) + log P(component)

Throws:

Exception - if not trained yet or something else went wrong

See Also:

AbstractMixtureModel.getNumberOfComponents()