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## de.jstacs.models.discrete.inhomogeneous
Class InhCondProb

    java.lang.Object
      de.jstacs.models.discrete.Constraint
          de.jstacs.models.discrete.inhomogeneous.InhConstraint
              de.jstacs.models.discrete.inhomogeneous.InhCondProb

All Implemented Interfaces:
  Storable, Cloneable

    public class InhCondProb

extends InhConstraint

This class handles (conditional) probabilities of sequences for ihnhomogeneous models.

Author:
  Jens Keilwagen

Field Summary

Fields inherited from class de.jstacs.models.discrete.inhomogeneous.InhConstraint
`offset`

Fields inherited from class de.jstacs.models.discrete.Constraint
`counts, freq, usedPositions`

Constructor Summary
`InhCondProb(int[] pos, int[] alphabetLength, boolean cond)` Creates a new `InhCondProb` instance.
`InhCondProb(int pos, int... alphabetLength)` Creates a new `InhCondProb` instance.
`InhCondProb(StringBuffer xml)` The standard constructor for the interface `Storable`.

Method Summary
`protected void` | `appendAdditionalInfo(StringBuffer xml)` This method appends additional information that is not stored in the base class to the `StringBuffer`.
`InhCondProb` | `clone()`
`void` | `drawParameters(double ess)` Draws the parameters from a Dirichlet distribution using the counts and the given `ess` (equivalent sample size) as hyperparameters.
`protected void` | `drawUnConditional(int start, int end, double pc)` This method draws the parameters for a part of this constraint.
`void` | `estimate(double ess)` Estimates the (smoothed) relative frequencies using the ess (equivalent sample size).
`void` | `estimateUnConditional(double ess, double all)` Estimates the unconditional frequencies using the ess (equivalent sample size).
`protected void` | `estimateUnConditional(int start, int end, double pc, boolean exceptionWhenNoData)` Estimates unconditionally.
`protected void` | `extractAdditionalInfo(StringBuffer xml)` This method parses additional information from the `StringBuffer` that is not parsed in the base class.
`double` | `getLnFreq(int index)` Returns the logarithm of the relative frequency (=probability) at position `index` in the distribution.
`double` | `getLnFreq(Sequence s, int start)` Returns the logarithm of the relative frequency (=probability) with the position in the distribution given by the index of the specific constraint that is fulfilled by the `Sequence` `s` beginning at `start`.
`void` | `getOutput(byte[] content, double p)` This method is used to create random sequences.
`protected String` | `getXMLTag()` Returns the XML tag that is used for the class to en- or decode.
`void` | `setFreqs(String[] array, int start)` This method is used to restore the values of a Gibbs Sampling run.
`String` | `toString()`

Methods inherited from class de.jstacs.models.discrete.inhomogeneous.InhConstraint
`satisfiesSpecificConstraint`

Methods inherited from class de.jstacs.models.discrete.Constraint
`add, add, getCount, getFreq, getFreq, getMarginalOrder, getNumberOfSpecificConstraints, getPosition, getPositions, reset, toXML`

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

Constructor Detail

### InhCondProb

    public InhCondProb(int pos,
                       int... alphabetLength)

  Creates a new `InhCondProb` instance.

  Parameters:
    `pos` - the position
    `alphabetLength` - the length of each alphabet (not only the used position)
  See Also:
    `InhCondProb(int[], int[], boolean)`

### InhCondProb

    public InhCondProb(int[] pos,
                       int[] alphabetLength,
                       boolean cond)

  Creates a new `InhCondProb` instance.

  Parameters:
    `pos` - the positions
    `alphabetLength` - the length of each alphabet (not only the used positions)
    `cond` - indicates if the instance has to use conditional probabilities
  See Also:
    `InhConstraint.InhConstraint(int[], int[])`

### InhCondProb

    public InhCondProb(StringBuffer xml)
                throws NonParsableException

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

  Parameters:
    `xml` - the XML representation as `StringBuffer`
  Throws:
    `NonParsableException` - if the `InhCondProb` instance could not be reconstructed out of the XML representation (the `StringBuffer` could not be parsed)
  See Also:
    `Storable`, `InhConstraint.InhConstraint(StringBuffer)`

Method Detail

### clone

    public InhCondProb clone()
                      throws CloneNotSupportedException

  Overrides:
    `clone` in class `InhConstraint`
  Throws:
    `CloneNotSupportedException`

### drawParameters

    public void drawParameters(double ess)

  Draws the parameters from a Dirichlet distribution using the counts and the given `ess` (equivalent sample size) as hyperparameters.

  Parameters:
    `ess` - the given ess (equivalent sample size)
  See Also:
    `drawUnConditional(int, int, double)`

### estimate

    public void estimate(double ess)

  Description copied from class: `Constraint`
  Estimates the (smoothed) relative frequencies using the ess (equivalent sample size).

  Specified by:
    `estimate` in class `Constraint`
  Parameters:
    `ess` - the ess

### estimateUnConditional

    public void estimateUnConditional(double ess,
                                      double all)

  Estimates the unconditional frequencies using the ess (equivalent sample size).

  Parameters:
    `ess` - the ess (equivalent sample size)
    `all` - the sum of all weights used to fill the counts

### getLnFreq

    public double getLnFreq(int index)

  Returns the logarithm of the relative frequency (=probability) at position `index` in the distribution.

  Parameters:
    `index` - the index of the entry in the distribution
  Returns:
    the logarithm of the relative frequency (=probability)

### getLnFreq

    public double getLnFreq(Sequence s,
                            int start)

  Returns the logarithm of the relative frequency (=probability) with the position in the distribution given by the index of the specific constraint that is fulfilled by the `Sequence` `s` beginning at `start`.

  Parameters:
    `s` - the sequence
    `start` - the index of the start position
  Returns:
    the logarithm of the relative frequency (=probability)
  See Also:
    `InhConstraint.satisfiesSpecificConstraint(Sequence, int)`

### getOutput

    public void getOutput(byte[] content,
                          double p)
                   throws OperationNotSupportedException

  This method is used to create random sequences.

  Parameters:
    `content` - the content of the random sequence as far as it is known
    `p` - a random number in (0,1)
  Throws:
    `OperationNotSupportedException` - if this instance models a joint probability for more than one position (shall be implemented in the future)
  See Also:
    `Model.emitSample(int, int...)`

### toString

    public String toString()

  Specified by:
    `toString` in class `Constraint`

### drawUnConditional

    protected void drawUnConditional(int start,
                                     int end,
                                     double pc)

  This method draws the parameters for a part of this constraint. It is used to draw from a distribution with fixed context.

  Parameters:
    `start` - the start index
    `end` - the end index
    `pc` - the pseudocount/hyperparameter

### estimateUnConditional

    protected void estimateUnConditional(int start,
                                         int end,
                                         double pc,
                                         boolean exceptionWhenNoData)

  Description copied from class: `Constraint`
  Estimates unconditionally.

  Overrides:
    `estimateUnConditional` in class `Constraint`
  Parameters:
    `start` - the start index
    `end` - the end index
    `pc` - the pseudocount for each parameter
    `exceptionWhenNoData` - indicates if an (runtime) exception is thrown if no data was available to estimate the parameters

### appendAdditionalInfo

    protected void appendAdditionalInfo(StringBuffer xml)

  Description copied from class: `Constraint`
  This method appends additional information that is not stored in the base class to the `StringBuffer`.

  Overrides:
    `appendAdditionalInfo` in class `InhConstraint`
  Parameters:
    `xml` - the `StringBuffer` that is used for appending additional information

### getXMLTag

    protected String getXMLTag()

  Description copied from class: `Constraint`
  Returns the XML tag that is used for the class to en- or decode.

  Specified by:
    `getXMLTag` in class `Constraint`
  Returns:
    the XML tag that is used for the class to en- or decode

### extractAdditionalInfo

    protected void extractAdditionalInfo(StringBuffer xml)
                                  throws NonParsableException

  Description copied from class: `Constraint`
  This method parses additional information from the `StringBuffer` that is not parsed in the base class.

  Overrides:
    `extractAdditionalInfo` in class `InhConstraint`
  Parameters:
    `xml` - the `StringBuffer` to be parsed
  Throws:
    `NonParsableException` - if something with the parsing went wrong

### setFreqs

    public void setFreqs(String[] array,
                         int start)
                  throws IllegalArgumentException

  This method is used to restore the values of a Gibbs Sampling run. The parameters are encoded in `String`s. Each `String` is one frequency. The index `start` is used to begin at a specific position in the array.

  Parameters:
    `array` - the array of `String` chunks to be parsed
    `start` - the start index
  Throws:
    `IllegalArgumentException` - if something is wrong with the frequencies

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