DifferentiableHigherOrderHMM

java.lang.Object
- de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
- - de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
  - - de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
    - - de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.DifferentiableHigherOrderHMM

All Implemented Interfaces:

DifferentiableSequenceScore, SequenceScore, DifferentiableStatisticalModel, SamplingDifferentiableStatisticalModel, StatisticalModel, TrainableStatisticalModel, Storable, Cloneable
```
public class DifferentiableHigherOrderHMM
extends HigherOrderHMM
implements SamplingDifferentiableStatisticalModel
```
This class combines an HigherOrderHMM and a DifferentiableStatisticalModel by implementing some of the declared methods.

Author:

Jens Keilwagen

Nested Class Summary
- Nested classes/interfaces inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
  HigherOrderHMM.Type

Field Summary

Fields
Modifier and Type	Field and Description
`protected double`	`ess` The equivalent sample size used for the prior
`protected double[][][]`	`gradient` Help array for the gradient
`protected int[][]`	`index` Index array used for computing the gradient
`protected IntList[]`	`indicesState` Help array for the indexes of the parameters of the states
`protected IntList[]`	`indicesTransition` Help array for the indexes of the parameters of the transition
`protected int`	`numberOfParameters` The number of parameters of this HMM
`protected DoubleList[]`	`partDerState` Help array for the derivatives of the parameters of the states
`protected DoubleList[]`	`partDerTransition` Help array for the derivatives of the parameters of the transition
`protected HigherOrderHMM.Type`	`score` The type of the score that is evaluated

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
backwardIntermediate, container, logEmission, numberOfSummands, skipInit, stateList

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
bwdMatrix, emission, emissionIdx, finalState, forward, fwdMatrix, name, sostream, START_NODE, states, threads, trainingParameter, transition

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
alphabets, length

Fields inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
UNKNOWN

Constructor Summary

Constructors
Constructor and Description
`DifferentiableHigherOrderHMM(MaxHMMTrainingParameterSet trainingParameterSet, String[] name, int[] emissionIdx, boolean[] forward, DifferentiableEmission[] emission, boolean likelihood, double ess, TransitionElement... te)` This is the main constructor.
`DifferentiableHigherOrderHMM(StringBuffer xml)` The standard constructor for the interface `Storable`.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`void`	`addGradientOfLogPriorTerm(double[] grad, int start)` This method computes the gradient of `DifferentiableStatisticalModel.getLogPriorTerm()` for each parameter of this model.
`protected void`	`appendFurtherInformation(StringBuffer xml)` This method appends further information to the XML representation.
`DifferentiableHigherOrderHMM`	`clone()` Follows the conventions of `Object`'s `clone()`-method.
`protected void`	`createHelperVariables()` This method instantiates all helper variables that are need inside the model for instance for filling forward and backward matrix, ...
`protected void`	`createStates()` This method creates states for the internal usage.
`protected void`	`extractFurtherInformation(StringBuffer xml)` This method extracts further information from the XML representation.
`double[]`	`getCurrentParameterValues()` Returns a `double` array of dimension `DifferentiableSequenceScore.getNumberOfParameters()` containing the current parameter values.
`double`	`getESS()` Returns the equivalent sample size (ess) of this model, i.e.
`double`	`getInitialClassParam(double classProb)` Returns the initial class parameter for the class this `DifferentiableSequenceScore` is responsible for, based on the class probability `classProb`.
`String`	`getInstanceName()` Should return a short instance name such as iMM(0), BN(2), ...
`double`	`getLogNormalizationConstant()` Returns the logarithm of the sum of the scores over all sequences of the event space.
`double`	`getLogPartialNormalizationConstant(int parameterIndex)` Returns the logarithm of the partial normalization constant for the parameter with index `parameterIndex`.
`double`	`getLogScoreAndPartialDerivation(Sequence seq, int startPos, int endPos, IntList indices, DoubleList partialDer)` Returns the logarithmic score for a `Sequence` beginning at position `start` in the `Sequence` and fills lists with the indices and the partial derivations.
`double`	`getLogScoreAndPartialDerivation(Sequence seq, int startPos, IntList indices, DoubleList partialDer)` Returns the logarithmic score for a `Sequence` beginning at position `start` in the `Sequence` and fills lists with the indices and the partial derivations.
`double`	`getLogScoreAndPartialDerivation(Sequence seq, IntList indices, DoubleList partialDer)` Returns the logarithmic score for a `Sequence` `seq` and fills lists with the indices and the partial derivations.
`double`	`getLogScoreFor(Sequence seq)` Returns the logarithmic score for the `Sequence` `seq`.
`double`	`getLogScoreFor(Sequence seq, int start)` Returns the logarithmic score for the `Sequence` `seq` beginning at position `start` in the `Sequence`.
`double`	`getLogScoreFor(Sequence seq, int start, int end)` Returns the logarithmic score for the `Sequence` `seq` beginning at position `start` in the `Sequence`.
`int`	`getNumberOfParameters()` Returns the number of parameters in this `DifferentiableSequenceScore`.
`int`	`getNumberOfRecommendedStarts()` This method returns the number of recommended optimization starts.
`int[][]`	`getSamplingGroups(int parameterOffset)` Returns groups of indexes of parameters that shall be drawn together in a sampling procedure
`int`	`getSizeOfEventSpaceForRandomVariablesOfParameter(int index)` Returns the size of the event space of the random variables that are affected by parameter no.
`void`	`initializeFunction(int index, boolean freeParams, DataSet[] data, double[][] weights)` This method creates the underlying structure of the `DifferentiableSequenceScore`.
`void`	`initializeFunctionRandomly(boolean freeParams)` This method initializes the `DifferentiableSequenceScore` randomly.
`boolean`	`isInitialized()` This method can be used to determine whether the instance is initialized.
`boolean`	`isNormalized()` This method indicates whether the implemented score is already normalized to 1 or not.
`protected double`	`logProb(int startpos, int endpos, Sequence sequence)` This method computes the logarithm of the probability of the corresponding subsequences.
`void`	`setParameters(double[] params, int start)` This method sets the internal parameters to the values of `params` between `start` and `start + DifferentiableSequenceScore.getNumberOfParameters() - 1`
`void`	`train(DataSet data, double[] weights)` Trains the `TrainableStatisticalModel` object given the data as `DataSet` using the specified weights.

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM
baumWelch, estimateFromStatistics, fillBwdMatrix, fillBwdOrViterbiMatrix, fillFwdMatrix, fillLogStatePosteriorMatrix, finalize, getCharacteristics, getEmissionIndexes, getEmissions, getLogPriorTerm, getLogProbForPath, getLogScoreFor, getLogScoreFor, getMaximalMarkovOrder, getNames, getNumericalCharacteristics, getTrainingParams, getTransisionElements, getViterbiPathFor, getXMLTag, initialize, initializeRandomly, resetStatistics, samplePath, setSkiptInit, viterbi

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM
createMatrixForStatePosterior, decodePath, decodeStatePosterior, determineFinalStates, fromXML, getFinalStatePosterioriMatrix, getGraphvizRepresentation, getGraphvizRepresentation, getGraphvizRepresentation, getGraphvizRepresentation, getLogProbFor, getLogStatePosteriorMatrixFor, getLogStatePosteriorMatrixFor, getNumberOfStates, getNumberOfThreads, getRunTimeException, getStatePosteriorMatrixFor, getStatePosteriorMatrixFor, getViterbiPathFor, getViterbiPathsFor, initTransition, provideMatrix, setOutputStream, toString, toXML, train

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.AbstractTrainableStatisticalModel
check, emitDataSet, getAlphabetContainer, getLength, getLogProbFor, getLogProbFor, toString

Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel
getLogPriorTerm

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel
emitDataSet, getLogProbFor, getLogProbFor, getLogProbFor, getMaximalMarkovOrder

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore
getAlphabetContainer, getCharacteristics, getLength, getLogScoreFor, getLogScoreFor, getNumericalCharacteristics, toString

Methods inherited from interface de.jstacs.Storable
toXML

- Field Detail
  - numberOfParameters
```
protected int numberOfParameters
```
    The number of parameters of this HMM
  - ess
```
protected double ess
```
    The equivalent sample size used for the prior
  - score
```
protected HigherOrderHMM.Type score
```
    The type of the score that is evaluated
  - index
```
protected int[][] index
```
    Index array used for computing the gradient
  - gradient
```
protected double[][][] gradient
```
    Help array for the gradient
  - indicesState
```
protected IntList[] indicesState
```
    Help array for the indexes of the parameters of the states
  - indicesTransition
```
protected IntList[] indicesTransition
```
    Help array for the indexes of the parameters of the transition
  - partDerState
```
protected DoubleList[] partDerState
```
    Help array for the derivatives of the parameters of the states
  - partDerTransition
```
protected DoubleList[] partDerTransition
```
    Help array for the derivatives of the parameters of the transition
- Constructor Detail
  - DifferentiableHigherOrderHMM
```
public DifferentiableHigherOrderHMM(MaxHMMTrainingParameterSet trainingParameterSet,
                                    String[] name,
                                    int[] emissionIdx,
                                    boolean[] forward,
                                    DifferentiableEmission[] emission,
                                    boolean likelihood,
                                    double ess,
                                    TransitionElement... te)
                             throws Exception
```
    This is the main constructor.
    Parameters:
    
    trainingParameterSet - the ParameterSet that determines the training algorithm and contains the necessary Parameters
    
    name - the names of the states
    
    emissionIdx - the indices of the emissions that should be used for each state, if null state i will use emission i
    
    forward - a boolean array that indicates whether the symbol on the forward or the reverse complementary strand should be used, if null all states use the forward strand
    
    emission - the emissions
    
    likelihood - if true the likelihood is return by getLogScoreFor(Sequence) otherwise the viterbi score
    
    ess - the ess of the model
    
    te - the TransitionElements used for creating a Transition
    
    Throws:
    Exception - if
    
    some component could not be cloned
    
    some the length of name, emissionIdx, or forward is not equal to the number of states
    
    not all emissions use the same AlphabetContainer
    
    the states can not be handled by the transition
  - DifferentiableHigherOrderHMM
```
public DifferentiableHigherOrderHMM(StringBuffer xml)
                             throws NonParsableException
```
    The standard constructor for the interface Storable. Constructs an DifferentiableHigherOrderHMM out of an XML representation.
    
    Parameters:
    
    xml - the XML representation as StringBuffer
    
    Throws:
    
    NonParsableException - if the DifferentiableHigherOrderHMM could not be reconstructed out of the StringBuffer xml
- Method Detail
  - appendFurtherInformation
```
protected void appendFurtherInformation(StringBuffer xml)
```
    Description copied from class: AbstractHMM
    
    This method appends further information to the XML representation. It allows subclasses to save further parameters that are not defined in the superclass.
    
    Overrides:
    
    appendFurtherInformation in class HigherOrderHMM
    
    Parameters:
    
    xml - the XML representation
  - extractFurtherInformation
```
protected void extractFurtherInformation(StringBuffer xml)
                                  throws NonParsableException
```
    Description copied from class: HigherOrderHMM
    
    This method extracts further information from the XML representation. It allows subclasses to cast further parameters that are not defined in the superclass.
    
    Overrides:
    
    extractFurtherInformation in class HigherOrderHMM
    
    Parameters:
    
    xml - the XML representation
    
    Throws:
    
    NonParsableException - if the information could not be reconstructed out of the StringBuffer xml
  - createHelperVariables
```
protected void createHelperVariables()
```
    Description copied from class: AbstractHMM
    
    This method instantiates all helper variables that are need inside the model for instance for filling forward and backward matrix, ...
    
    Overrides:
    
    createHelperVariables in class HigherOrderHMM
  - createStates
```
protected void createStates()
```
    Description copied from class: AbstractHMM
    
    This method creates states for the internal usage.
    
    Overrides:
    
    createStates in class HigherOrderHMM
  - clone
```
public DifferentiableHigherOrderHMM clone()
                                   throws CloneNotSupportedException
```
    Description copied from class: AbstractTrainableStatisticalModel
    
    Follows the conventions of Object's clone()-method.
    
    Specified by:
    
    clone in interface DifferentiableSequenceScore
    
    Specified by:
    
    clone in interface SequenceScore
    
    Specified by:
    
    clone in interface TrainableStatisticalModel
    
    Overrides:
    
    clone in class HigherOrderHMM
    
    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
  - getESS
```
public double getESS()
```
    Description copied from interface: DifferentiableStatisticalModel
    
    Returns the equivalent sample size (ess) of this model, i.e. the equivalent sample size for the class or component that is represented by this model.
    
    Specified by:
    
    getESS in interface DifferentiableStatisticalModel
    
    Returns:
    
    the equivalent sample size.
  - addGradientOfLogPriorTerm
```
public void addGradientOfLogPriorTerm(double[] grad,
                                      int start)
                               throws Exception
```
    Description copied from interface: DifferentiableStatisticalModel
    
    This method computes the gradient of DifferentiableStatisticalModel.getLogPriorTerm() for each parameter of this model. The results are added to the array grad beginning at index start.
    
    Specified by:
    
    addGradientOfLogPriorTerm in interface DifferentiableStatisticalModel
    
    Parameters:
    
    grad - the array of gradients
    
    start - the start index in the grad array, where the partial derivations for the parameters of this models shall be entered
    
    Throws:
    
    Exception - if something went wrong with the computing of the gradients
    
    See Also:
    
    DifferentiableStatisticalModel.getLogPriorTerm()
  - getNumberOfParameters
```
public int getNumberOfParameters()
```
    Description copied from interface: DifferentiableSequenceScore
    
    Returns the number of parameters in this DifferentiableSequenceScore. If the number of parameters is not known yet, the method returns DifferentiableSequenceScore.UNKNOWN.
    
    Specified by:
    
    getNumberOfParameters in interface DifferentiableSequenceScore
    
    Returns:
    
    the number of parameters in this DifferentiableSequenceScore
    
    See Also:
    
    DifferentiableSequenceScore.UNKNOWN
  - getNumberOfRecommendedStarts
```
public int getNumberOfRecommendedStarts()
```
    Description copied from interface: DifferentiableSequenceScore
    
    This method returns the number of recommended optimization starts. The standard implementation returns 1.
    
    Specified by:
    
    getNumberOfRecommendedStarts in interface DifferentiableSequenceScore
    
    Returns:
    
    the number of recommended optimization starts
  - getCurrentParameterValues
```
public double[] getCurrentParameterValues()
                                   throws Exception
```
    Description copied from interface: DifferentiableSequenceScore
    
    Returns a double array of dimension DifferentiableSequenceScore.getNumberOfParameters() containing the current parameter values. If one likes to use these parameters to start an optimization it is highly recommended to invoke DifferentiableSequenceScore.initializeFunction(int, boolean, DataSet[], double[][]) before. After an optimization this method can be used to get the current parameter values.
    
    Specified by:
    
    getCurrentParameterValues in interface DifferentiableSequenceScore
    
    Returns:
    
    the current parameter values
    
    Throws:
    
    Exception - if no parameters exist (yet)
  - isInitialized
```
public boolean isInitialized()
```
    Description copied from interface: SequenceScore
    
    This method can be used to determine whether the instance is initialized. If the instance is initialized you should be able to invoke SequenceScore.getLogScoreFor(Sequence).
    
    Specified by:
    
    isInitialized in interface SequenceScore
    
    Overrides:
    
    isInitialized in class HigherOrderHMM
    
    Returns:
    
    true if the instance is initialized, false otherwise
  - setParameters
```
public void setParameters(double[] params,
                          int start)
```
    Description copied from interface: DifferentiableSequenceScore
    
    This method sets the internal parameters to the values of params between start and start + DifferentiableSequenceScore.getNumberOfParameters() - 1
    
    Specified by:
    
    setParameters in interface DifferentiableSequenceScore
    
    Parameters:
    
    params - the new parameters
    
    start - the start index in params
  - initializeFunctionRandomly
```
public void initializeFunctionRandomly(boolean freeParams)
                                throws Exception
```
    Description copied from interface: DifferentiableSequenceScore
    
    This method initializes the DifferentiableSequenceScore randomly. It has to create the underlying structure of the DifferentiableSequenceScore.
    
    Specified by:
    
    initializeFunctionRandomly in interface DifferentiableSequenceScore
    
    Parameters:
    
    freeParams - indicates whether the (reduced) parameterization is used
    
    Throws:
    
    Exception - if something went wrong
  - initializeFunction
```
public void initializeFunction(int index,
                               boolean freeParams,
                               DataSet[] data,
                               double[][] weights)
                        throws Exception
```
    Description copied from interface: DifferentiableSequenceScore
    
    This method creates the underlying structure of the DifferentiableSequenceScore.
    
    Specified by:
    
    initializeFunction in interface DifferentiableSequenceScore
    
    Parameters:
    
    index - the index of the class the DifferentiableSequenceScore models
    
    freeParams - indicates whether the (reduced) parameterization is used
    
    data - the data sets
    
    weights - the weights of the sequences in the data sets
    
    Throws:
    
    Exception - if something went wrong
  - 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 HigherOrderHMM
    
    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
  - isNormalized
```
public boolean isNormalized()
```
    Description copied from interface: DifferentiableStatisticalModel
    
    This method indicates whether the implemented score is already normalized to 1 or not. The standard implementation returns false.
    
    Specified by:
    
    isNormalized in interface DifferentiableStatisticalModel
    
    Returns:
    
    true if the implemented score is already normalized to 1, false otherwise
  - getLogNormalizationConstant
```
public double getLogNormalizationConstant()
```
    Description copied from interface: DifferentiableStatisticalModel
    
    Returns the logarithm of the sum of the scores over all sequences of the event space.
    
    Specified by:
    
    getLogNormalizationConstant in interface DifferentiableStatisticalModel
    
    Returns:
    
    the logarithm of the normalization constant Z
  - getLogPartialNormalizationConstant
```
public double getLogPartialNormalizationConstant(int parameterIndex)
                                          throws Exception
```
    Description copied from interface: DifferentiableStatisticalModel
    
    Returns the logarithm of the partial normalization constant for the parameter with index parameterIndex. This is the logarithm of the partial derivation of the normalization constant for the parameter with index parameterIndex,
    $\log \frac{\partial Z(\underline{\lambda})}{\partial \lambda_{parameterindex}}$
    .
    
    Specified by:
    
    getLogPartialNormalizationConstant in interface DifferentiableStatisticalModel
    
    Parameters:
    
    parameterIndex - the index of the parameter
    
    Returns:
    
    the logarithm of the partial normalization constant
    
    Throws:
    
    Exception - if something went wrong with the normalization
    
    See Also:
    
    DifferentiableStatisticalModel.getLogNormalizationConstant()
  - getInitialClassParam
```
public double getInitialClassParam(double classProb)
```
    Description copied from interface: DifferentiableSequenceScore
    
    Returns the initial class parameter for the class this DifferentiableSequenceScore is responsible for, based on the class probability classProb.
    
    Specified by:
    
    getInitialClassParam in interface DifferentiableSequenceScore
    
    Parameters:
    
    classProb - the class probability
    
    Returns:
    
    the initial class parameter
  - getLogScoreFor
```
public double getLogScoreFor(Sequence seq)
```
    Description copied from interface: SequenceScore
    
    Returns the logarithmic score for the Sequence seq.
    
    Specified by:
    
    getLogScoreFor in interface SequenceScore
    
    Overrides:
    
    getLogScoreFor in class AbstractTrainableStatisticalModel
    
    Parameters:
    
    seq - the sequence
    
    Returns:
    
    the logarithmic score for the sequence
  - getLogScoreFor
```
public double getLogScoreFor(Sequence seq,
                             int start)
```
    Description copied from interface: SequenceScore
    
    Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.
    
    Specified by:
    
    getLogScoreFor in interface SequenceScore
    
    Overrides:
    
    getLogScoreFor in class AbstractTrainableStatisticalModel
    
    Parameters:
    
    seq - the Sequence
    
    start - the start position in the Sequence
    
    Returns:
    
    the logarithmic score for the Sequence
  - getLogScoreFor
```
public double getLogScoreFor(Sequence seq,
                             int start,
                             int end)
```
    Description copied from interface: SequenceScore
    
    Returns the logarithmic score for the Sequence seq beginning at position start in the Sequence.
    
    Specified by:
    
    getLogScoreFor in interface SequenceScore
    
    Overrides:
    
    getLogScoreFor in class AbstractTrainableStatisticalModel
    
    Parameters:
    
    seq - the Sequence
    
    start - the start position in the Sequence
    
    end - the end position (inclusive) in the Sequence
    
    Returns:
    
    the logarithmic score for the Sequence
  - logProb
```
protected double logProb(int startpos,
                         int endpos,
                         Sequence sequence)
```
    Description copied from class: AbstractHMM
    
    This method computes the logarithm of the probability of the corresponding subsequences. The method does not check the AlphabetContainer and possible further features before starting the computation.
    
    Overrides:
    
    logProb in class AbstractHMM
    
    Parameters:
    
    startpos - the start position (inclusive)
    
    endpos - the end position (inclusive)
    
    sequence - the Sequence(s)
    
    Returns:
    
    the logarithm of the probability
  - getLogScoreAndPartialDerivation
```
public double getLogScoreAndPartialDerivation(Sequence seq,
                                              IntList indices,
                                              DoubleList partialDer)
```
    Description copied from interface: DifferentiableSequenceScore
    
    Returns the logarithmic score for a Sequence seq and fills lists with the indices and the partial derivations.
    
    Specified by:
    
    getLogScoreAndPartialDerivation in interface DifferentiableSequenceScore
    
    Parameters:
    
    seq - the Sequence
    
    indices - an IntList of indices, after method invocation the list should contain the indices i where $\frac{\partial \log score(seq)}{\partial \lambda_i}$ is not zero
    
    partialDer - a DoubleList of partial derivations, after method invocation the list should contain the corresponding $\frac{\partial \log score(seq)}{\partial \lambda_i}$ that are not zero
    
    Returns:
    
    the logarithmic score for the Sequence
  - getLogScoreAndPartialDerivation
```
public double getLogScoreAndPartialDerivation(Sequence seq,
                                              int startPos,
                                              IntList indices,
                                              DoubleList partialDer)
```
    Description copied from interface: DifferentiableSequenceScore
    
    Returns the logarithmic score for a Sequence beginning at position start in the Sequence and fills lists with the indices and the partial derivations.
    
    Specified by:
    
    getLogScoreAndPartialDerivation in interface DifferentiableSequenceScore
    
    Parameters:
    
    seq - the Sequence
    
    startPos - the start position in the Sequence
    
    indices - an IntList of indices, after method invocation the list should contain the indices i where $\frac{\partial \log score(seq)}{\partial \lambda_i}$ is not zero
    
    partialDer - a DoubleList of partial derivations, after method invocation the list should contain the corresponding $\frac{\partial \log score(seq)}{\partial \lambda_i}$ that are not zero
    
    Returns:
    
    the logarithmic score for the Sequence
  - getLogScoreAndPartialDerivation
```
public double getLogScoreAndPartialDerivation(Sequence seq,
                                              int startPos,
                                              int endPos,
                                              IntList indices,
                                              DoubleList partialDer)
```
    Description copied from interface: DifferentiableSequenceScore
    
    Returns the logarithmic score for a Sequence beginning at position start in the Sequence and fills lists with the indices and the partial derivations.
    
    Specified by:
    
    getLogScoreAndPartialDerivation in interface DifferentiableSequenceScore
    
    Parameters:
    
    seq - the Sequence
    
    startPos - the start position in the Sequence
    
    endPos - the end position (inclusive) in the Sequence
    
    indices - an IntList of indices, after method invocation the list should contain the indices i where $\frac{\partial \log score(seq)}{\partial \lambda_i}$ is not zero
    
    partialDer - a DoubleList of partial derivations, after method invocation the list should contain the corresponding $\frac{\partial \log score(seq)}{\partial \lambda_i}$ that are not zero
    
    Returns:
    
    the logarithmic score for the Sequence
  - getSizeOfEventSpaceForRandomVariablesOfParameter
```
public int getSizeOfEventSpaceForRandomVariablesOfParameter(int index)
```
    Description copied from interface: DifferentiableStatisticalModel
    
    Returns the size of the event space of the random variables that are affected by parameter no. index, i.e. the product of the sizes of the alphabets at the position of each random variable affected by parameter index. For DNA alphabets this corresponds to 4 for a PWM, 16 for a WAM except position 0, ...
    
    Specified by:
    
    getSizeOfEventSpaceForRandomVariablesOfParameter in interface DifferentiableStatisticalModel
    
    Parameters:
    
    index - the index of the parameter
    
    Returns:
    
    the size of the event space
  - getSamplingGroups
```
public int[][] getSamplingGroups(int parameterOffset)
```
    Description copied from interface: SamplingDifferentiableStatisticalModel
    
    Returns groups of indexes of parameters that shall be drawn together in a sampling procedure
    
    Specified by:
    
    getSamplingGroups in interface SamplingDifferentiableStatisticalModel
    
    Parameters:
    
    parameterOffset - a global offset on the parameter indexes
    
    Returns:
    
    the groups of indexes. The first dimension represents the different groups while the second dimension contains the parameters that shall be sampled together. Internal parameter indexes need to be increased by parameterOffset.
  - 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 HigherOrderHMM
    
    Returns:
    
    a short instance name

Class DifferentiableHigherOrderHMM

Nested Class Summary

Nested classes/interfaces inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM

Field Summary

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM

Fields inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM

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

Fields inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore

Constructor Summary

Method Summary

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.models.HigherOrderHMM

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.trainable.hmm.AbstractHMM

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

Methods inherited from class java.lang.Object

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.differentiable.DifferentiableStatisticalModel

Methods inherited from interface de.jstacs.sequenceScores.statisticalModels.StatisticalModel

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

Methods inherited from interface de.jstacs.Storable

Field Detail

numberOfParameters

ess

score

index

gradient

indicesState

indicesTransition

partDerState

partDerTransition

Constructor Detail

DifferentiableHigherOrderHMM

DifferentiableHigherOrderHMM

Method Detail

appendFurtherInformation

extractFurtherInformation

createHelperVariables

createStates

clone

getESS

addGradientOfLogPriorTerm

getNumberOfParameters

getNumberOfRecommendedStarts

getCurrentParameterValues

isInitialized

setParameters

initializeFunctionRandomly

initializeFunction

train

isNormalized

getLogNormalizationConstant

getLogPartialNormalizationConstant

getInitialClassParam

getLogScoreFor

getLogScoreFor

getLogScoreFor

logProb

getLogScoreAndPartialDerivation

getLogScoreAndPartialDerivation

getLogScoreAndPartialDerivation

getSizeOfEventSpaceForRandomVariablesOfParameter

getSamplingGroups

getInstanceName