LimitedSparseLocalInhomogeneousMixtureDiffSM

java.lang.Object
- de.jstacs.sequenceScores.differentiable.AbstractDifferentiableSequenceScore
- - de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
  - - de.jstacs.sequenceScores.statisticalModels.differentiable.localMixture.LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder

All Implemented Interfaces:

Mutable, DifferentiableSequenceScore, QuickScanningSequenceScore, SequenceScore, DifferentiableStatisticalModel, StatisticalModel, Storable, Cloneable
```
public class LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
extends AbstractDifferentiableStatisticalModel
implements Mutable, QuickScanningSequenceScore
```
Class for a sparse local inhomogeneous mixture (Slim) model. The Slim model may be of higher order (i.e., considering multiple preceding positions) and the number of preceding positions that are considered may be limited (LSlim model).

Author:

Jens Keilwagen, Jan Grau

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder.PriorType
The type of the prior used by the Slim model

Nested Classes
Modifier and Type	Class and Description
`static class`	`LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder.PriorType` The type of the prior used by the Slim model

Field Summary
- 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
`LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder(AlphabetContainer alphabets, int length, int order, int distance, double ess, double q, LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder.PriorType t)` Creates a new Slim model with given number of components and maximum distance.
`LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder(StringBuffer xml)` Creates a `LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder` model from its XML representation

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.
`LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder`	`clone()` Creates a clone (deep copy) of the current `DifferentiableSequenceScore` instance.
`void`	`fillInfixScore(int[] seq, int start, int length, double[] scores)` Computes the position-wise scores of an infix of the sequence `seq` (which must be encoded by the same alphabet as this `QuickScanningSequenceScore`) beginning at `start` and extending for `length` positions.
`protected void`	`fromXML(StringBuffer xml)` This method is called in the constructor for the `Storable` interface to create a scoring function from a `StringBuffer`.
`double[][]`	`getAncestorProbabilities(int component)` Returns the probabilities that the preceding positions considered are used as context.
`double[][][]`	`getConditionalProbabilities(int component)` Returns the conditional probabilities for the specified component.
`protected double[][]`	`getCum_Complex(int kmer)`
`protected double[][]`	`getCum_Naive(int kmer)`
`double[]`	`getCurrentParameterValues()` Returns a `double` array of dimension `DifferentiableSequenceScore.getNumberOfParameters()` containing the current parameter values.
`int`	`getDistance()` Returns the maximum distance of preceding positions considered in the LSlim model.
`double`	`getESS()` Returns the equivalent sample size (ess) of this model, i.e.
`String`	`getGraphviz()` Returns a Graphviz (dot) representation of the Slim model.
`boolean[][]`	`getInfixFilter(int kmer, double thresh, int... start)` Computes arrays that indicate, for a given set of starting positions and a given k-mer length, if a sequence containing this k-mer may yield a score above `threshold`, choosing the best-scoring option among all non-specified positions (i.e., those outside the k-mer).
`protected void`	`getInfixScores(int s, int start, int l, int kmer, int[] seq, double[][] prefixScore, double[][] max)`
`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`	`getLogScoreAndPartialDerivation(Sequence seq, int start, 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`	`getLogScoreFor(Sequence seq, int start)` Returns the logarithmic score for the `Sequence` `seq` beginning at position `start` in the `Sequence`.
`double[][]`	`getMixtureProbabilities()` Returns the probabilities of the mixture components.
`int`	`getNumberOfParameters()` Returns the number of parameters in this `DifferentiableSequenceScore`.
`int`	`getNumberOfRecommendedStarts()` This method returns the number of recommended optimization starts.
`int`	`getOrder()` Returns the order of the Slim model
`double[][]`	`getPWMParameters()` Returns the unconditional, normalized (PWM) probabilities of this Slim model
`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`	`modify(int offsetLeft, int offsetRight)` Manually modifies the model.
`void`	`set(int position, double[] pars)` Sets the (conditional) probability parameters at a specific position and sets the mixture parameters (largely) to the unconditional PWM component.
`void`	`setParameters(double[] params, int start)` This method sets the internal parameters to the values of `params` between `start` and `start + DifferentiableSequenceScore.getNumberOfParameters() - 1`
`String`	`toString(NumberFormat nf)` This method returns a `String` representation of the instance.
`StringBuffer`	`toXML()` This method returns an XML representation as `StringBuffer` of an instance of the implementing class.

Methods inherited from class de.jstacs.sequenceScores.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel
emitDataSet, getInitialClassParam, getLogProbFor, getLogProbFor, getLogProbFor, getLogScoreFor, getLogScoreFor, getMaximalMarkovOrder, isNormalized, isNormalized

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

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

- Constructor Detail
  - LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
```
public LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder(AlphabetContainer alphabets,
                                                                int length,
                                                                int order,
                                                                int distance,
                                                                double ess,
                                                                double q,
                                                                LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder.PriorType t)
                                                         throws IllegalArgumentException
```
    Creates a new Slim model with given number of components and maximum distance. Uses the BDeu prior.
    
    Parameters:
    
    alphabets - the alphabet of sequences the model is defined on
    
    length - the length of the sequences that may be scores
    
    order - the number of components, i.e., the number of preceding positions considered jointly
    
    distance - the maximum distance of preceding positions considered
    
    ess - the equivalent sample size
    
    q - Parameter q of the mixture prior, ignored for BDeu prior
    
    t - the type of the prior
    
    Throws:
    
    IllegalArgumentException - if the ess or other parameters are not allowed
  - LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder
```
public LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder(StringBuffer xml)
                                                         throws NonParsableException
```
    Creates a LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder model from its XML representation
    
    Parameters:
    
    xml - the XML representation
    
    Throws:
    
    NonParsableException - if XML could not be parsed
- Method Detail
  - getOrder
```
public int getOrder()
```
    Returns the order of the Slim model
    
    Returns:
    
    the order
  - getDistance
```
public int getDistance()
```
    Returns the maximum distance of preceding positions considered in the LSlim model.
    
    Returns:
    
    the maximum distance
  - clone
```
public LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder clone()
                                                               throws CloneNotSupportedException
```
    Description copied from interface: DifferentiableSequenceScore
    
    Creates a clone (deep copy) of the current DifferentiableSequenceScore instance.
    
    Specified by:
    
    clone in interface DifferentiableSequenceScore
    
    Specified by:
    
    clone in interface SequenceScore
    
    Overrides:
    
    clone in class AbstractDifferentiableStatisticalModel
    
    Returns:
    
    the cloned instance of the current DifferentiableSequenceScore
    
    Throws:
    
    CloneNotSupportedException - if something went wrong while cloning the DifferentiableSequenceScore
  - 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()
  - 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
  - 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()
  - 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.
  - getInfixFilter
```
public boolean[][] getInfixFilter(int kmer,
                                  double thresh,
                                  int... start)
```
    Description copied from interface: QuickScanningSequenceScore
    
    Computes arrays that indicate, for a given set of starting positions and a given k-mer length, if a sequence containing this k-mer may yield a score above threshold, choosing the best-scoring option among all non-specified positions (i.e., those outside the k-mer). This method is implemented as an upper bound on the scores, i.e., there may be k-mers that are considered to score above threshold (i.e., that have entry true) although they are not, but there may not be k-mers that are considered to be below threshold (i.e., that have entry false), although there exist sequences containing this k-mer that do. The returned array is indexed by the starting positions (in the same order as provided in starts) in the first dimension, and in the second dimension it is indexed by an integer representation of the k-mers, assigning the highest priority to the first k-mer position, i.e.,
    $\sum_{\ell=0}^{L-1} A^{L-1-\ell} \cdot e(x_\ell)$
    where denotes the size of the alphabet, the length of the k-mer (starting at 0 in this case), and $e(\ldots)$ denotes the function encoding symbols from the alphabet as integers (see DiscreteAlphabet).
    
    Specified by:
    
    getInfixFilter in interface QuickScanningSequenceScore
    
    Parameters:
    
    kmer - the k-mer length
    
    thresh - the threshold
    
    start - the starting position(s)
    
    Returns:
    
    the indicator arrays
  - getCum_Naive
```
protected double[][] getCum_Naive(int kmer)
```
  - getCum_Complex
```
protected double[][] getCum_Complex(int kmer)
```
  - getInfixScores
```
protected void getInfixScores(int s,
                              int start,
                              int l,
                              int kmer,
                              int[] seq,
                              double[][] prefixScore,
                              double[][] max)
```
  - 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
    
    Parameters:
    
    seq - the Sequence
    
    start - the start position in the Sequence
    
    Returns:
    
    the logarithmic score for the Sequence
  - getLogScoreAndPartialDerivation
```
public double getLogScoreAndPartialDerivation(Sequence seq,
                                              int start,
                                              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
    
    start - 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
  - 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
  - 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)
  - set
```
public void set(int position,
                double[] pars)
```
    Sets the (conditional) probability parameters at a specific position and sets the mixture parameters (largely) to the unconditional PWM component.
    
    Parameters:
    
    position - the position
    
    pars - the new parameters at this position
  - 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
  - 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
    
    Returns:
    
    a short instance name
  - 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
    
    Returns:
    
    true if the instance is initialized, false otherwise
  - toXML
```
public StringBuffer toXML()
```
    Description copied from interface: Storable
    
    This method returns an XML representation as StringBuffer of an instance of the implementing class.
    
    Specified by:
    
    toXML in interface Storable
    
    Returns:
    
    the XML representation
  - fromXML
```
protected void fromXML(StringBuffer xml)
                throws NonParsableException
```
    Description copied from class: AbstractDifferentiableSequenceScore
    
    This method is called in the constructor for the Storable interface to create a scoring function from a StringBuffer.
    
    Specified by:
    
    fromXML in class AbstractDifferentiableSequenceScore
    
    Parameters:
    
    xml - the XML representation as StringBuffer
    
    Throws:
    
    NonParsableException - if the StringBuffer could not be parsed
    
    See Also:
    
    AbstractDifferentiableSequenceScore.AbstractDifferentiableSequenceScore(StringBuffer)
  - toString
```
public String toString(NumberFormat nf)
```
    Description copied from interface: SequenceScore
    
    This method returns a String representation of the instance.
    
    Specified by:
    
    toString in interface SequenceScore
    
    Parameters:
    
    nf - the NumberFormat for the String representation of parameters or probabilities
    
    Returns:
    
    a String representation of the instance
  - getConditionalProbabilities
```
public double[][][] getConditionalProbabilities(int component)
                                         throws CloneNotSupportedException
```
    Returns the conditional probabilities for the specified component.
    
    Parameters:
    
    component - the component
    
    Returns:
    
    the conditional probabilities
    
    Throws:
    
    CloneNotSupportedException - if the internal probabilities could not be cloned
  - getPWMParameters
```
public double[][] getPWMParameters()
                            throws CloneNotSupportedException
```
    Returns the unconditional, normalized (PWM) probabilities of this Slim model
    
    Returns:
    
    the PWM probabilities
    
    Throws:
    
    CloneNotSupportedException - if the internal parameters could not be cloned
  - getMixtureProbabilities
```
public double[][] getMixtureProbabilities()
                                   throws CloneNotSupportedException
```
    Returns the probabilities of the mixture components.
    
    Returns:
    
    the probabilities of the mixture components
    
    Throws:
    
    CloneNotSupportedException - if the internal parameters could not be cloned
  - getAncestorProbabilities
```
public double[][] getAncestorProbabilities(int component)
```
    Returns the probabilities that the preceding positions considered are used as context.
    
    Parameters:
    
    component - the component considered
    
    Returns:
    
    the probabilities of the preceding positions
  - getGraphviz
```
public String getGraphviz()
```
    Returns a Graphviz (dot) representation of the Slim model.
    
    Returns:
    
    the Graphviz representation
  - 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 AbstractDifferentiableSequenceScore
    
    Returns:
    
    the number of recommended optimization starts
  - modify
```
public boolean modify(int offsetLeft,
                      int offsetRight)
```
    Description copied from interface: Mutable
    
    Manually modifies the model. 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.
    
    Specified by:
    
    modify in interface Mutable
    
    Parameters:
    
    offsetLeft - the offset on the left side
    
    offsetRight - the offset on the right side
    
    Returns:
    
    true if the motif model was modified otherwise false
  - fillInfixScore
```
public void fillInfixScore(int[] seq,
                           int start,
                           int length,
                           double[] scores)
```
    Description copied from interface: QuickScanningSequenceScore
    
    Computes the position-wise scores of an infix of the sequence seq (which must be encoded by the same alphabet as this QuickScanningSequenceScore) beginning at start and extending for length positions. The scores are computed per position and filled into the provided array scores, which is of the same length as seq. This must be implemented such that ToolBox.sum(double...) applied to scores computed from 0 to seq.length returns the same value as SequenceScore.getLogScoreFor(de.jstacs.data.sequences.Sequence) called on the IntSequence created from seq.
    
    Specified by:
    
    fillInfixScore in interface QuickScanningSequenceScore
    
    Parameters:
    
    seq - the sequence
    
    start - the start of the infix
    
    length - the length of the infix
    
    scores - the array of scores to be (partly) filled

Class LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder

Nested Class Summary

Field Summary

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.statisticalModels.differentiable.AbstractDifferentiableStatisticalModel

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

Methods inherited from class java.lang.Object

Methods inherited from interface de.jstacs.sequenceScores.SequenceScore

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

Constructor Detail

LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder

LimitedSparseLocalInhomogeneousMixtureDiffSM_higherOrder

Method Detail

getOrder

getDistance

clone

getSizeOfEventSpaceForRandomVariablesOfParameter

getLogNormalizationConstant

getLogPartialNormalizationConstant

initializeFunctionRandomly

initializeFunction

getLogPriorTerm

addGradientOfLogPriorTerm

getESS

getInfixFilter

getCum_Naive

getCum_Complex

getInfixScores

getLogScoreFor

getLogScoreAndPartialDerivation

getNumberOfParameters

getCurrentParameterValues

set

setParameters

getInstanceName

isInitialized

toXML

fromXML

toString

getConditionalProbabilities

getPWMParameters

getMixtureProbabilities

getAncestorProbabilities

getGraphviz

getNumberOfRecommendedStarts

modify

fillInfixScore