IndependentProductDiffSM

java.lang.Object
- de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
- - de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  - - de.jstacs.sequenceScores.statisticalModels.differentiable.IndependentProductDiffSM

All Implemented Interfaces:

MotifDiscoverer, MutableMotifDiscoverer, DifferentiableSequenceScore, SequenceScore, DifferentiableStatisticalModel, StatisticalModel, Storable, Cloneable
```
public class IndependentProductDiffSM
extends IndependentProductDiffSS
implements DifferentiableStatisticalModel, MutableMotifDiscoverer
```
This class enables the user to model parts of a sequence independent of each other. For instance, the first part of the sequence is modeled by the first DifferentiableStatisticalModel and has the length of the first DifferentiableStatisticalModel, the second part starts directly after the first part, is modeled by the second DifferentiableStatisticalModel ... etc. It is also possible to use a DifferentiableStatisticalModel for more than one sequence part and in both orientations (if possible).

It is important to set the equivalent sample size (ESS) of each instance carefully, i.e., corresponding to the ESS of the parts.

Author:

Jens Keilwagen

Nested Class Summary
- Nested classes/interfaces inherited from interface de.jstacs.motifDiscovery.MotifDiscoverer
  MotifDiscoverer.KindOfProfile

Field Summary
- Fields inherited from class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
  index, isVariable, partialLength, reverse, score, start, startIndexOfParams
- Fields inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
  alphabets, length, r
- Fields inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
  UNKNOWN

Constructor Summary

Constructors
Constructor and Description
`IndependentProductDiffSM(double ess, boolean plugIn, DifferentiableStatisticalModel... functions)` This constructor creates an instance of an `IndependentProductDiffSM` from a given series of independent `DifferentiableStatisticalModel`s.
`IndependentProductDiffSM(double ess, boolean plugIn, DifferentiableStatisticalModel[] functions, int[] length)` This constructor creates an instance of an `IndependentProductDiffSM` from given series of independent `DifferentiableStatisticalModel`s and lengths.
`IndependentProductDiffSM(double ess, boolean plugIn, DifferentiableStatisticalModel[] functions, int[] index, int[] length, boolean[] reverse)` This is the main constructor.
`IndependentProductDiffSM(StringBuffer source)` This is the constructor for the interface `Storable`.

Method Summary

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.
`void`	`adjustHiddenParameters(int index, DataSet[] data, double[][] weights)` Adjusts all hidden parameters including duration and mixture parameters according to the current values of the remaining parameters.
`IndependentProductDiffSM`	`clone()` Creates a clone (deep copy) of the current `DifferentiableSequenceScore` instance.
`DataSet`	`emitDataSet(int numberOfSequences, int... seqLength)` This method returns a `DataSet` object containing artificial sequence(s).
`protected void`	`extractFurtherInformation(StringBuffer rep)` This method is the opposite of `IndependentProductDiffSS.getFurtherInformation()`.
`double`	`getESS()` Returns the equivalent sample size (ess) of this model, i.e.
`protected StringBuffer`	`getFurtherInformation()` This method is used to append further information of the instance to the XML representation.
`int`	`getGlobalIndexOfMotifInComponent(int component, int motif)` Returns the global index of the `motif` used in `component`.
`int`	`getIndexOfMaximalComponentFor(Sequence sequence)` Returns the index of the component with the maximal score for the sequence `sequence`.
`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`	`getLogPriorTerm()` This method computes a value that is proportional to
`double`	`getLogProbFor(Sequence sequence)` Returns the logarithm of the probability of the given sequence given the model.
`double`	`getLogProbFor(Sequence sequence, int startpos)` Returns the logarithm of the probability of (a part of) the given sequence given the model.
`double`	`getLogProbFor(Sequence sequence, int startpos, int endpos)` Returns the logarithm of the probability of (a part of) the given sequence given the model.
`byte`	`getMaximalMarkovOrder()` This method returns the maximal used Markov order, if possible.
`int`	`getMotifLength(int motif)` This method returns the length of the motif with index `motif` .
`int`	`getNumberOfComponents()` Returns the number of components in this `MotifDiscoverer`.
`int`	`getNumberOfMotifs()` Returns the number of motifs for this `MotifDiscoverer`.
`int`	`getNumberOfMotifsInComponent(int component)` Returns the number of motifs that are used in the component `component` of this `MotifDiscoverer`.
`int`	`getNumberOfParameters()` Returns the number of parameters in this `DifferentiableSequenceScore`.
`int`	`getNumberOfRecommendedStarts()` This method returns the number of recommended optimization starts.
`double[]`	`getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile dist)` Returns the profile of the scores for component `component` and motif `motif` at all possible start positions of the motif in the sequence `sequence` beginning at `startpos`.
`int`	`getSizeOfEventSpaceForRandomVariablesOfParameter(int index)` Returns the size of the event space of the random variables that are affected by parameter no.
`double[]`	`getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos)` This method returns the probabilities of the strand orientations for a given subsequence if it is considered as site of the motif model in a specific component.
`void`	`initializeMotif(int motifIndex, DataSet data, double[] weights)` This method allows to initialize the model of a motif manually using a weighted data set.
`void`	`initializeMotifRandomly(int motif)` This method initializes the motif with index `motif` randomly using for instance `DifferentiableSequenceScore.initializeFunctionRandomly(boolean)`.
`boolean`	`isNormalized()` This method indicates whether the implemented score is already normalized to 1 or not.
`boolean`	`modifyMotif(int motifIndex, int offsetLeft, int offsetRight)` Manually modifies the motif model with index `motifIndex`.
`void`	`setParameters(double[] params, int start)` This method sets the internal parameters to the values of `params` between `start` and `start + DifferentiableSequenceScore.getNumberOfParameters() - 1`

Methods inherited from class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS
extractSequenceParts, extractWeights, fromXML, getCurrentParameterValues, getFunctions, getIndices, getLengthArray, getLogScoreAndPartialDerivation, getLogScoreFor, getPartialLengths, getReverseSwitches, initializeFunction, initializeFunctionRandomly, isInitialized, setParamsStarts, toString, toXML

Methods inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
getAlphabetContainer, getCharacteristics, getInitialClassParam, getLength, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreFor, getLogScoreFor, getLogScoreFor, getLogScoreFor, getNumberOfStarts, getNumericalCharacteristics, toString

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

Methods inherited from interface de.jstacs.sequenceScores.differentiable.DifferentiableSequenceScore
getCurrentParameterValues, getInitialClassParam, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, getLogScoreAndPartialDerivation, initializeFunction, initializeFunctionRandomly

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

Methods inherited from interface de.jstacs.Storable
toXML

- Constructor Detail
  - IndependentProductDiffSM
```
public IndependentProductDiffSM(double ess,
                        boolean plugIn,
                        DifferentiableStatisticalModel... functions)
                         throws CloneNotSupportedException,
                                IllegalArgumentException,
                                WrongAlphabetException
```
    This constructor creates an instance of an IndependentProductDiffSM from a given series of independent DifferentiableStatisticalModels. The length that is modeled by each component is determined by SequenceScore.getLength(). So the length should not be 0.
    
    Parameters:
    ess - the equivalent sample size
    plugIn - whether to use plugIn parameters for the parts, otherwise the last parameters are used for parts that are instance of HomogeneousDiffSM
    functions - the components, i.e. the given series of independent DifferentiableStatisticalModels
    
    Throws:
    
    CloneNotSupportedException - if at least one element of functions could not be cloned
    
    IllegalArgumentException - if at least one component has length 0 or if the equivalent sample size (ess) is smaller than zero (0)
    
    WrongAlphabetException - if the user tries to use an alphabet for a reverse complement that can not be used for a reverse complement.
    See Also:
    IndependentProductDiffSM(double, boolean, DifferentiableStatisticalModel[], int[])
  - IndependentProductDiffSM
```
public IndependentProductDiffSM(double ess,
                        boolean plugIn,
                        DifferentiableStatisticalModel[] functions,
                        int[] length)
                         throws CloneNotSupportedException,
                                IllegalArgumentException,
                                WrongAlphabetException
```
    This constructor creates an instance of an IndependentProductDiffSM from given series of independent DifferentiableStatisticalModels and lengths.
    
    Parameters:
    ess - the equivalent sample size
    plugIn - whether to use plugIn parameters for the parts, otherwise the last parameters are used for parts that are instance of HomogeneousDiffSM
    functions - the components, i.e. the given series of independent DifferentiableStatisticalModels
    length - the lengths, one for each component
    
    Throws:
    
    CloneNotSupportedException - if at least one component could not be cloned
    
    IllegalArgumentException - if the lengths and the components are not matching or if the equivalent sample size (ess) is smaller than zero (0)
    
    WrongAlphabetException - if the user tries to use an alphabet for a reverse complement that can not be used for a reverse complement.
    See Also:
    IndependentProductDiffSM(double, boolean, DifferentiableStatisticalModel[], int[], int[], boolean[])
  - IndependentProductDiffSM
```
public IndependentProductDiffSM(double ess,
                        boolean plugIn,
                        DifferentiableStatisticalModel[] functions,
                        int[] index,
                        int[] length,
                        boolean[] reverse)
                         throws CloneNotSupportedException,
                                IllegalArgumentException,
                                WrongAlphabetException
```
    This is the main constructor.
    
    Parameters:
    ess - the equivalent sample size
    plugIn - whether to use plugIn parameters for the parts, otherwise the last parameters are used for parts that are instance of HomogeneousDiffSM
    functions - the DifferentiableStatisticalModel
    index - the index of the DifferentiableStatisticalModel at each part
    length - the length of each part
    reverse - a switch whether to use it directly or the reverse complementary strand
    
    Throws:
    
    CloneNotSupportedException - if at least one component could not be cloned
    
    IllegalArgumentException - if the lengths and the components are not matching or if the equivalent sample size (ess) is smaller than zero (0)
    
    WrongAlphabetException - if the user tries to use an alphabet for a reverse complement that can not be used for a reverse complement.
  - IndependentProductDiffSM
```
public IndependentProductDiffSM(StringBuffer source)
                         throws NonParsableException
```
    This is the constructor for the interface Storable. Creates a new IndependentProductDiffSM out of a StringBuffer as returned by IndependentProductDiffSS.toXML().
    
    Parameters:
    source - the XML representation as StringBuffer
    
    Throws:
    
    NonParsableException - if the XML representation could not be parsed
- Method Detail
  - clone
```
public IndependentProductDiffSM clone()
                               throws CloneNotSupportedException
```
    Description copied from interface: DifferentiableSequenceScore
    
    Creates a clone (deep copy) of the current DifferentiableSequenceScore instance.
    
    Specified by:
    
    clone in interface MotifDiscoverer
    
    Specified by:
    
    clone in interface DifferentiableSequenceScore
    
    Specified by:
    
    clone in interface SequenceScore
    
    Overrides:
    
    clone in class IndependentProductDiffSS
    
    Returns:
    the cloned instance of the current DifferentiableSequenceScore
    
    Throws:
    
    CloneNotSupportedException - if something went wrong while cloning the DifferentiableSequenceScore
    See Also:
    Cloneable
  - 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
  - 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()
  - 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.
  - extractFurtherInformation
```
protected void extractFurtherInformation(StringBuffer rep)
                                  throws NonParsableException
```
    Description copied from class: IndependentProductDiffSS
    
    This method is the opposite of IndependentProductDiffSS.getFurtherInformation(). It extracts further information of the instance from a XML representation.
    
    Overrides:
    
    extractFurtherInformation in class IndependentProductDiffSS
    
    Parameters:
    rep - the StringBuffer containing the information to be extracted as XML code
    
    Throws:
    
    NonParsableException - if the StringBuffer could not be parsed
  - 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 IndependentProductDiffSS
    
    Returns:
    a short instance name
  - 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
    
    Overrides:
    
    getNumberOfParameters in class IndependentProductDiffSS
    
    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
    
    Overrides:
    
    getNumberOfRecommendedStarts in class IndependentProductDiffSS
    
    Returns:
    the number of recommended optimization starts
  - 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
    
    Overrides:
    
    setParameters in class IndependentProductDiffSS
    
    Parameters:
    params - the new parameters
    start - the start index in params
  - getFurtherInformation
```
protected StringBuffer getFurtherInformation()
```
    Description copied from class: IndependentProductDiffSS
    
    This method is used to append further information of the instance to the XML representation. This method is designed to allow subclasses to add information to the XML representation.
    
    Overrides:
    
    getFurtherInformation in class IndependentProductDiffSS
    
    Returns:
    the further information as XML code in a StringBuffer
  - getLogPriorTerm
```
public double getLogPriorTerm()
```
    Description copied from interface: DifferentiableStatisticalModel
    
    This method computes a value that is proportional to
    DifferentiableStatisticalModel.getESS() * DifferentiableStatisticalModel.getLogNormalizationConstant() + Math.log( prior )
    where prior is the prior for the parameters of this model.
    
    Specified by:
    
    getLogPriorTerm in interface DifferentiableStatisticalModel
    
    Specified by:
    
    getLogPriorTerm in interface StatisticalModel
    
    Returns:
    a value that is proportional to DifferentiableStatisticalModel.getESS() * DifferentiableStatisticalModel.getLogNormalizationConstant() + Math.log( prior ).
    See Also:
    DifferentiableStatisticalModel.getESS(), DifferentiableStatisticalModel.getLogNormalizationConstant()
  - 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()
  - getLogProbFor
```
public double getLogProbFor(Sequence sequence,
                   int startpos)
                     throws Exception
```
    Description copied from interface: StatisticalModel
    
    Returns the logarithm of the probability of (a part of) the given sequence given the model. If at least one random variable is continuous the value of density function is returned.
    
    If the length of the sequences, whose probability should be returned, is fixed (e.g. in a inhomogeneous model) and the given sequence is longer than their fixed length, the start position within the given sequence is given by startpos. E.g. the fixed length is 12. The length of the given sequence is 30 and the startpos=15 the logarithm of the probability of the part from position 15 to 26 (inclusive) given the model should be returned.
    The length and the alphabets define the type of data that can be modeled and therefore both has to be checked.
    
    Specified by:
    
    getLogProbFor in interface StatisticalModel
    
    Parameters:
    sequence - the given sequence
    startpos - the start position within the given sequence
    
    Returns:
    the logarithm of the probability or the value of the density function of (the part of) the given sequence given the model
    
    Throws:
    
    Exception - if the sequence could not be handled by the model
    
    NotTrainedException - if the model is not trained yet
    See Also:
    StatisticalModel.getLogProbFor(Sequence, int, int)
  - getLogProbFor
```
public double getLogProbFor(Sequence sequence)
                     throws Exception
```
    Description copied from interface: StatisticalModel
    
    Returns the logarithm of the probability of the given sequence given the model. If at least one random variable is continuous the value of density function is returned.
    
    The length and the alphabets define the type of data that can be modeled and therefore both has to be checked.
    
    Specified by:
    
    getLogProbFor in interface StatisticalModel
    
    Parameters:
    sequence - the given sequence for which the logarithm of the probability/the value of the density function should be returned
    
    Returns:
    the logarithm of the probability or the value of the density function of the part of the given sequence given the model
    
    Throws:
    
    Exception - if the sequence could not be handled by the model
    
    NotTrainedException - if the model is not trained yet
    See Also:
    StatisticalModel.getLogProbFor(Sequence, int, int)
  - getLogProbFor
```
public double getLogProbFor(Sequence sequence,
                   int startpos,
                   int endpos)
                     throws Exception
```
    Description copied from interface: StatisticalModel
    
    Returns the logarithm of the probability of (a part of) the given sequence given the model. If at least one random variable is continuous the value of density function is returned.
    
    It extends the possibility given by the method StatisticalModel.getLogProbFor(Sequence, int) by the fact, that the model could be e.g. homogeneous and therefore the length of the sequences, whose probability should be returned, is not fixed. Additionally, the end position of the part of the given sequence is given and the probability of the part from position startpos to endpos (inclusive) should be returned.
    The length and the alphabets define the type of data that can be modeled and therefore both has to be checked.
    
    Specified by:
    
    getLogProbFor in interface StatisticalModel
    
    Parameters:
    sequence - the given sequence
    startpos - the start position within the given sequence
    endpos - the last position to be taken into account
    
    Returns:
    the logarithm of the probability or the value of the density function of (the part of) the given sequence given the model
    
    Throws:
    
    Exception - if the sequence could not be handled (e.g. startpos > , endpos > sequence.length, ...) by the model
    
    NotTrainedException - if the model is not trained yet
  - initializeMotif
```
public void initializeMotif(int motifIndex,
                   DataSet data,
                   double[] weights)
                     throws Exception
```
    Description copied from interface: MutableMotifDiscoverer
    
    This method allows to initialize the model of a motif manually using a weighted data set.
    
    Specified by:
    
    initializeMotif in interface MutableMotifDiscoverer
    
    Parameters:
    motifIndex - the index of the motif in the motif discoverer
    data - the data set of sequences
    weights - either null or an array of length data.getNumberofElements() with non-negative weights.
    
    Throws:
    
    Exception - if initialize was not possible
  - initializeMotifRandomly
```
public void initializeMotifRandomly(int motif)
                             throws Exception
```
    Description copied from interface: MutableMotifDiscoverer
    
    This method initializes the motif with index motif randomly using for instance DifferentiableSequenceScore.initializeFunctionRandomly(boolean). Furthermore, if available, it also initializes the positional distribution.
    
    Specified by:
    
    initializeMotifRandomly in interface MutableMotifDiscoverer
    
    Parameters:
    motif - the index of the motif
    
    Throws:
    
    Exception - either if the index is wrong or if it is thrown by the method DifferentiableSequenceScore.initializeFunctionRandomly(boolean)
  - modifyMotif
```
public boolean modifyMotif(int motifIndex,
                  int offsetLeft,
                  int offsetRight)
                    throws Exception
```
    Description copied from interface: MutableMotifDiscoverer
    
    Manually modifies the motif model with index motifIndex. The two offsets offsetLeft and offsetRight define how many positions the left or right border positions shall be moved. Negative numbers indicate moves to the left while positive numbers correspond to moves to the right. The distribution for sequences to the left and right side of the motif shall be computed internally.
    
    Specified by:
    
    modifyMotif in interface MutableMotifDiscoverer
    
    Parameters:
    motifIndex - the index of the motif in the motif discoverer
    offsetLeft - the offset on the left side
    offsetRight - the offset on the right side
    
    Returns:
    true if the motif model was modified otherwise false
    
    Throws:
    
    Exception - if some unexpected error occurred during the modification
    See Also:
    MutableMotifDiscoverer.modifyMotif(int, int, int), Mutable.modify(int, int)
  - getGlobalIndexOfMotifInComponent
```
public int getGlobalIndexOfMotifInComponent(int component,
                                   int motif)
```
    Description copied from interface: MotifDiscoverer
    
    Returns the global index of the motif used in component. The index returned must be at least 0 and less than MotifDiscoverer.getNumberOfMotifs().
    
    Specified by:
    
    getGlobalIndexOfMotifInComponent in interface MotifDiscoverer
    
    Parameters:
    component - the component index
    motif - the motif index in the component
    
    Returns:
    the global index of the motif in component
  getIndexOfMaximalComponentFor public int getIndexOfMaximalComponentFor(Sequence sequence) throws Exception Description copied from interface: MotifDiscoverer Returns the index of the component with the maximal score for the sequence sequence. Specified by: getIndexOfMaximalComponentFor in interface MotifDiscoverer Parameters: sequence - the given sequence Returns: the index of the component with the maximal score for the given sequence Throws: Exception - if the index could not be computed for any reasons getMotifLength public int getMotifLength(int motif) Description copied from interface: MotifDiscoverer This method returns the length of the motif with index motif . Specified by: getMotifLength in interface MotifDiscoverer Parameters: motif - the index of the motif Returns: the length of the motif with index motif getNumberOfComponents public int getNumberOfComponents() Description copied from interface: MotifDiscoverer Returns the number of components in this MotifDiscoverer. Specified by: getNumberOfComponents in interface MotifDiscoverer Returns: the number of components getNumberOfMotifs public int getNumberOfMotifs() Description copied from interface: MotifDiscoverer Returns the number of motifs for this MotifDiscoverer. Specified by: getNumberOfMotifs in interface MotifDiscoverer Returns: the number of motifs getNumberOfMotifsInComponent public int getNumberOfMotifsInComponent(int component) Description copied from interface: MotifDiscoverer Returns the number of motifs that are used in the component component of this MotifDiscoverer. Specified by: getNumberOfMotifsInComponent in interface MotifDiscoverer Parameters: component - the component of the MotifDiscoverer Returns: the number of motifs getProfileOfScoresFor public double[] getProfileOfScoresFor(int component, int motif, Sequence sequence, int startpos, MotifDiscoverer.KindOfProfile dist) throws Exception Description copied from interface: MotifDiscoverer Returns the profile of the scores for component component and motif motif at all possible start positions of the motif in the sequence sequence beginning at startpos. This array should be of length sequence.length() - startpos - motifs[motif].getLength() + 1. A high score should encode for a probable start position. Specified by: getProfileOfScoresFor in interface MotifDiscoverer Parameters: component - the component index motif - the index of the motif in the component sequence - the given sequence startpos - the start position in the sequence dist - indicates the kind of profile Returns: the profile of scores Throws: Exception - if the score could not be computed for any reasons getStrandProbabilitiesFor public double[] getStrandProbabilitiesFor(int component, int motif, Sequence sequence, int startpos) throws Exception Description copied from interface: MotifDiscoverer This method returns the probabilities of the strand orientations for a given subsequence if it is considered as site of the motif model in a specific component. Specified by: getStrandProbabilitiesFor in interface MotifDiscoverer Parameters: component - the component index motif - the index of the motif in the component sequence - the given sequence startpos - the start position in the sequence Returns: the probabilities of the strand orientations Throws: Exception - if the strand could not be computed for any reasons 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 adjustHiddenParameters public void adjustHiddenParameters(int index, DataSet[] data, double[][] weights) throws Exception Description copied from interface: MutableMotifDiscoverer Adjusts all hidden parameters including duration and mixture parameters according to the current values of the remaining parameters. Specified by: adjustHiddenParameters in interface MutableMotifDiscoverer Parameters: index - the index of the class of this MutableMotifDiscoverer data - the array of data for all classes weights - the weights for all sequences in data Throws: Exception - thrown if the hidden parameters could not be adjusted emitDataSet public DataSet emitDataSet(int numberOfSequences, int... seqLength) throws NotTrainedException, Exception Description copied from interface: StatisticalModel This method returns a DataSet object containing artificial sequence(s). There are two different possibilities to create a data set for a model with length 0 (homogeneous models). emitDataSet( int n, int l ) should return a data set with n sequences of length l. emitDataSet( int n, int[] l ) should return a data set with n sequences which have a sequence length corresponding to the entry in the given array l. There are two different possibilities to create a data set for a model with length greater than 0 (inhomogeneous models). emitDataSet( int n ) and emitDataSet( int n, null ) should return a data set with n sequences of length of the model ( SequenceScore.getLength()). The standard implementation throws an Exception. Specified by: emitDataSet in interface StatisticalModel Parameters: numberOfSequences - the number of sequences that should be contained in the returned data set seqLength - the length of the sequences for a homogeneous model; for an inhomogeneous model this parameter should be null or an array of size 0. Returns: a DataSet containing the artificial sequence(s) Throws: NotTrainedException - if the model is not trained yet Exception - if the emission did not succeed See Also: DataSet getMaximalMarkovOrder public byte getMaximalMarkovOrder() throws UnsupportedOperationException Description copied from interface: StatisticalModel This method returns the maximal used Markov order, if possible. Specified by: getMaximalMarkovOrder in interface StatisticalModel Returns: maximal used Markov order Throws: UnsupportedOperationException - if the model can't give a proper answer

Class IndependentProductDiffSM

Nested Class Summary

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

Field Summary

Fields inherited from class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS

Fields inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore

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

Constructor Summary

Method Summary

Methods inherited from class de.jstacs.sequenceScores.differentiable.IndependentProductDiffSS

Methods inherited from class de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

Methods inherited from interface de.jstacs.Storable

Constructor Detail

IndependentProductDiffSM

IndependentProductDiffSM

IndependentProductDiffSM

IndependentProductDiffSM

Method Detail

clone

getSizeOfEventSpaceForRandomVariablesOfParameter

getLogNormalizationConstant

getLogPartialNormalizationConstant

getESS

extractFurtherInformation

getInstanceName

getNumberOfParameters

getNumberOfRecommendedStarts

setParameters

getFurtherInformation

getLogPriorTerm

addGradientOfLogPriorTerm

getLogProbFor

getLogProbFor

getLogProbFor

initializeMotif

initializeMotifRandomly

modifyMotif

getGlobalIndexOfMotifInComponent

getIndexOfMaximalComponentFor

getMotifLength

getNumberOfComponents

getNumberOfMotifs

getNumberOfMotifsInComponent

getProfileOfScoresFor

getStrandProbabilitiesFor

isNormalized

adjustHiddenParameters

emitDataSet

getMaximalMarkovOrder