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## de.jstacs.scoringFunctions.directedGraphicalModels
Class ParameterTree

    java.lang.Object
      de.jstacs.scoringFunctions.directedGraphicalModels.ParameterTree

All Implemented Interfaces:
  Storable, Cloneable

    public class ParameterTree

extends Object
implements Cloneable, Storable

Class for the tree that represents the context of a `Parameter` in a `BayesianNetworkScoringFunction`.

Author:
  Jan Grau

Nested Class Summary
`class` | `ParameterTree.TreeElement` Class for the nodes of a `ParameterTree`

Constructor Summary
`ParameterTree(int pos, int[] contextPoss, AlphabetContainer alphabet, int firstParent, int[] firstChildren)` Creates a new `ParameterTree` for the parameters at position `pos` using the parent positions in `contextPoss`.
`ParameterTree(StringBuffer source)` Recreates a `ParameterTree` from its XML representation as returned by `toXML()`.

Method Summary
`void` | `addCount(Sequence seq, int start, double count)` Adds `count` to the parameter as returned by `getParameterFor(Sequence, int)`.
`void` | `backward(ParameterTree[] trees, int[][] order)` Computes the backward-part of the normalization constant starting from this `ParameterTree`.
`ParameterTree` | `clone()`
`Double` | `computeGammaNorm()` Computes the Gamma-normalization for the prior.
`void` | `copy(ParameterTree parameterTree)` Copies the values of the parameters from another `ParameterTree`.
`void` | `divideByUnfree()` Divides each of the normalized parameters on a simplex by the last `Parameter`, which is defined not to be free.
`void` | `drawKLDivergences(double[] kls, double[] weights, double[][][][] contrast, double samples)` Draws KL-divergences between the distributions given by `contrast[i]` each weighted by `weights[i]` `kls.length` distributions drawn from a Dirichlet density centered around `contrast`, i.e. the hyper-parameters of the Dirichlet density are the probabilities of `contrast` weighted by `samples`.
`void` | `drawKLDivergences(double weight, double[] kls, int startIdx, int endIdx, double[][][] contrast, double samples)` Draws KL-divergences between the distribution given by `contrast` and `endIdx-startIdx` distributions drawn from a Dirichlet density centered around `contrast`, i.e. the hyper-parameters of the Dirichlet density are the probabilities of `contrast` weighted by `samples`.
`void` | `fill(double[] weight, double[][][][] distribution)` Fills all parameters with the probabilities given in `distribution`.
`double` | `forward(ParameterTree[] trees)` Computes the forward-part of the normalization constant starting from this `ParameterTree`.
`int` | `getFirstParent()` Returns the first parent of the random variable of this `ParameterTree` in the topological ordering of the network structure of the enclosing `BayesianNetworkScoringFunction`.
`double` | `getKLDivergence(double[][][] ds)` Returns the KL-divergence of the distribution of this `ParameterTree` and the distribution given by `ds`.
`double` | `getKLDivergence(double[] weight, double[][][][] distribution)` Returns the KL-divergence of the distribution of this `ParameterTree` and a number of distribution given by `ds` and weighted by `weight`
`int` | `getNumberOfParents()` Returns the number of parents for the random variable of this `ParameterTree` in the network structure of the enclosing `BayesianNetworkScoringFunction`.
`Parameter` | `getParameterFor(Sequence seq, int start)` Returns the `Parameter` that is responsible for the suffix of sequence `seq` starting at position `start`.
`int[]` | `getParameterIndexesForSamplingStep(int step, int offset)` Returns the indexes of the parameters, incremented by `offset`, that shall be sampled in step `step` of a grouped sampling process.
`double` | `getProbFor(Sequence sequence)` Returns the probability of `Sequence` `sequence` in this `ParameterTree`.
`void` | `initializeRandomly(double ess)` Initializes the parameters of this `ParameterTree` randomly.
`void` | `insertProbs(double[] probs)` Computes the probabilities for a PWM, i.e. the parameters in the tree have an empty context, and inserts them into `probs`.
`void` | `invalidateNormalizers()` Resets all pre-computed normalization constants.
`boolean` | `isLeaf()` Indicates if the random variable of this `ParameterTree` is a leaf, i.e. it has no children in the network structure of the enclosing `BayesianNetworkScoringFunction`.
`LinkedList<Parameter>` | `linearizeParameters()` Extracts the `Parameter`s from the leaves of this tree in left-to-right order (as specified by the order of the alphabet) and returns them as a `LinkedList`.
`void` | `normalizeParameters()` Normalizes the parameter values to the corresponding log-probabilities.
`void` | `normalizePlugInParameters()` Starts the normalization of the plug-in parameters to the logarithm of the MAP-estimates.
`void` | `print()` Prints the structure of this tree.
`void` | `setParameterFor(int symbol, int[][] context, Parameter par)` Sets the instance of the `Parameter` for symbol `symbol` and context `context` to `Parameter` `par`.
`String` | `toString()`
`StringBuffer` | `toXML()` Works as defined in `Storable`.

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

Constructor Detail

### ParameterTree

    public ParameterTree(int pos,
                         int[] contextPoss,
                         AlphabetContainer alphabet,
                         int firstParent,
                         int[] firstChildren)

  Creates a new `ParameterTree` for the parameters at position `pos` using the parent positions in `contextPoss`. These are used to extract the correct alphabet out of `alphabet` for every context position. The first parent is the first parent of the random variable at `pos` as given by the topological ordering of the network structure of the enclosing `BayesianNetworkScoringFunction`. The first children are the children the random variable at `pos` is the first parent for.

  Parameters:
    `pos` - the position of the random variable of the parameters in the tree
    `contextPoss` - the positions of the context
    `alphabet` - the alphabet of the enclosing `BayesianNetworkScoringFunction`
    `firstParent` - the first parent of this random variable, or `-1` if the random variable has no parent
    `firstChildren` - the first children of this random variable

### ParameterTree

    public ParameterTree(StringBuffer source)
                  throws NonParsableException

  Recreates a `ParameterTree` from its XML representation as returned by `toXML()`. `ParameterTree` does not implement the `Storable` interface to recycle the `AlphabetContainer` of the enclosing `BayesianNetworkScoringFunction`, but besides the different constructor works like any implementation of `Storable`.

  Parameters:
    `source` - the XML representation as `StringBuffer`
  Throws:
    `NonParsableException` - if the XML code could not be parsed

Method Detail

### clone

    public ParameterTree clone()
                        throws CloneNotSupportedException

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

### toString

    public String toString()

  Overrides:
    `toString` in class `Object`

### insertProbs

    public void insertProbs(double[] probs)
                     throws Exception

  Computes the probabilities for a PWM, i.e. the parameters in the tree have an empty context, and inserts them into `probs`. Used by `BayesianNetworkScoringFunction.getPWM()`.

  Parameters:
    `probs` - the array to store the probabilities for a PWM
  Throws:
    `Exception` - if the tree structure does have a non-empty context

### linearizeParameters

    public LinkedList<Parameter> linearizeParameters()

  Extracts the `Parameter`s from the leaves of this tree in left-to-right order (as specified by the order of the alphabet) and returns them as a `LinkedList`.

  Returns:
    the `Parameter`s from the leaves in linear order

### isLeaf

    public boolean isLeaf()

  Indicates if the random variable of this `ParameterTree` is a leaf, i.e. it has no children in the network structure of the enclosing `BayesianNetworkScoringFunction`.

  Returns:
    `true` if this tree is a leaf, `false` otherwise

### getNumberOfParents

    public int getNumberOfParents()

  Returns the number of parents for the random variable of this `ParameterTree` in the network structure of the enclosing `BayesianNetworkScoringFunction`. This corresponds to the length of the context or the depth of the tree.

  Returns:
    the number of parents

### print

    public void print()

  Prints the structure of this tree.

### getParameterFor

    public Parameter getParameterFor(Sequence seq,
                                     int start)

  Returns the `Parameter` that is responsible for the suffix of sequence `seq` starting at position `start`.

  Parameters:
    `seq` - the `Sequence`
    `start` - the first position in the suffix
  Returns:
    the `Parameter` that is responsible for the suffix

### setParameterFor

    public void setParameterFor(int symbol,
                                int[][] context,
                                Parameter par)

  Sets the instance of the `Parameter` for symbol `symbol` and context `context` to `Parameter` `par`.

  Parameters:
    `symbol` - the symbol
    `context` - the context
    `par` - the new `Parameter` instance

### invalidateNormalizers

    public void invalidateNormalizers()

  Resets all pre-computed normalization constants.

### forward

    public double forward(ParameterTree[] trees)
                   throws RuntimeException

  Computes the forward-part of the normalization constant starting from this `ParameterTree`. This is only possible for roots, i.e. `ParameterTree`s that do not have parents in the network structure of the enclosing `BayesianNetworkScoringFunction`.

  Parameters:
    `trees` - the array of all trees as from the enclosing `BayesianNetworkScoringFunction`
  Returns:
    the forward-part of the normalization constant
  Throws:
    `RuntimeException` - if this `ParameterTree` is not a root

### backward

    public void backward(ParameterTree[] trees,
                         int[][] order)
                  throws RuntimeException

  Computes the backward-part of the normalization constant starting from this `ParameterTree`. This is only possible for leaves, i.e. `ParameterTree`s that do not have children in the network structure of the enclosing `BayesianNetworkScoringFunction`.

  Parameters:
    `trees` - the array of all trees as from the enclosing `BayesianNetworkScoringFunction`
    `order` - the topological ordering as returned by `TopSort.getTopologicalOrder(int[][])`
  Throws:
    `RuntimeException` - if this `ParameterTree` is not a leaf

### addCount

    public void addCount(Sequence seq,
                         int start,
                         double count)

  Adds `count` to the parameter as returned by `getParameterFor(Sequence, int)`.

  Parameters:
    `seq` - the sequence
    `start` - the first position of the suffix of `seq`
    `count` - the added count

### normalizePlugInParameters

    public void normalizePlugInParameters()

  Starts the normalization of the plug-in parameters to the logarithm of the MAP-estimates.

### normalizeParameters

    public void normalizeParameters()

  Normalizes the parameter values to the corresponding log-probabilities. After this step, `BayesianNetworkScoringFunction.getLogNormalizationConstant()` should return 0.

### divideByUnfree

    public void divideByUnfree()

  Divides each of the normalized parameters on a simplex by the last `Parameter`, which is defined not to be free. In the log-space this amounts to subtracting the value of the last `Parameter` from all `Parameter`s on the simplex.

### toXML

    public StringBuffer toXML()

  Works as defined in `Storable`. Returns an XML representation of this `ParameterTree`.

  Specified by:
    `toXML` in interface `Storable`
  Returns:
    the XML representation of this `ParameterTree`
  See Also:
    `Storable.toXML()`

### getFirstParent

    public int getFirstParent()

  Returns the first parent of the random variable of this `ParameterTree` in the topological ordering of the network structure of the enclosing `BayesianNetworkScoringFunction`.

  Returns:
    the first parent

### drawKLDivergences

    public void drawKLDivergences(double weight,
                                  double[] kls,
                                  int startIdx,
                                  int endIdx,
                                  double[][][] contrast,
                                  double samples)

  Draws KL-divergences between the distribution given by `contrast` and `endIdx-startIdx` distributions drawn from a Dirichlet density centered around `contrast`, i.e. the hyper-parameters of the Dirichlet density are the probabilities of `contrast` weighted by `samples`. The drawn KL-divergences are stored into `kls` between `startIndex` and `endIndex` (exclusive).

  Parameters:
    `weight` - a weight on the KL-divergences
    `kls` - the array of KL-divergences which is filled
    `startIdx` - the first index
    `endIdx` - the index after the last index
    `contrast` - the distribution to check against
    `samples` - number of sequences + equivalent sample size

### getKLDivergence

    public double getKLDivergence(double[][][] ds)

  Returns the KL-divergence of the distribution of this `ParameterTree` and the distribution given by `ds`.

  Parameters:
    `ds` - the distribution
  Returns:
    the KL-divergence

### getKLDivergence

    public double getKLDivergence(double[] weight,
                                  double[][][][] distribution)

  Returns the KL-divergence of the distribution of this `ParameterTree` and a number of distribution given by `ds` and weighted by `weight`

  Parameters:
    `distribution` - the distribution
    `weight` - the weights on the distributions
  Returns:
    the KL-divergence

### drawKLDivergences

    public void drawKLDivergences(double[] kls,
                                  double[] weights,
                                  double[][][][] contrast,
                                  double samples)

  Draws KL-divergences between the distributions given by `contrast[i]` each weighted by `weights[i]` `kls.length` distributions drawn from a Dirichlet density centered around `contrast`, i.e. the hyper-parameters of the Dirichlet density are the probabilities of `contrast` weighted by `samples`. The drawn KL-divergences are added to the entries of `kls`.

  Parameters:
    `kls` - the array of KL-divergences which is filled
    `weights` - the weights on the distributions in contrast
    `contrast` - the distribution to check against
    `samples` - number of sequences + equivalent sample size

### fill

    public void fill(double[] weight,
                     double[][][][] distribution)

  Fills all parameters with the probabilities given in `distribution`.

  Parameters:
    `distribution` - the distribution
    `weight` - the weights on the distributions

### copy

    public void copy(ParameterTree parameterTree)

  Copies the values of the parameters from another `ParameterTree`.

  Parameters:
    `parameterTree` - the template

### initializeRandomly

    public void initializeRandomly(double ess)

  Initializes the parameters of this `ParameterTree` randomly.

  Parameters:
    `ess` - the equivalent sample size

### computeGammaNorm

    public Double computeGammaNorm()

  Computes the Gamma-normalization for the prior.

  Returns:
    the Gamma-normalization

### getProbFor

    public double getProbFor(Sequence sequence)

  Returns the probability of `Sequence` `sequence` in this `ParameterTree`.

  Parameters:
    `sequence` - the sequence
  Returns:
    the probability

### getParameterIndexesForSamplingStep

    public int[] getParameterIndexesForSamplingStep(int step,
                                                    int offset)

  Returns the indexes of the parameters, incremented by `offset`, that shall be sampled in step `step` of a grouped sampling process.

  Parameters:
    `step` - the step
    `offset` - the offset on the parameter indexes
  Returns:
    the indexes of the group of parameters

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SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD