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## de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous
Class StructureLearner

    java.lang.Object
      de.jstacs.sequenceScores.statisticalModels.trainable.discrete.inhomogeneous.StructureLearner

    public class StructureLearner

extends Object

This class can be used to learn the structure of any discrete model.

Author:
  Jens Keilwagen

Nested Class Summary
`static class` | `StructureLearner.LearningType` This `enum` defines the different types of learning that are possible with the `StructureLearner`.
`static class` | `StructureLearner.ModelType` This `enum` defines the different types of models that can be learned with the `StructureLearner`.

Constructor Summary
`StructureLearner(AlphabetContainer con, int length)` Creates a `StructureLearner` with equivalent sample size (ess) = 0.
`StructureLearner(AlphabetContainer con, int length, double ess)` Creates a new `StructureLearner` for a given `AlphabetContainer`, a given length and a given equivalent sample size (ess).

Method Summary
`AlphabetContainer` | `getAlphabetContainer()` This method returns the `AlphabetContainer` of the `StructureLearner`.
`double` | `getEss()` This method returns the ess (equivalent sample size) of the `StructureLearner`.
`int[][]` | `getStructure(DataSet data, double[] weights, StructureLearner.ModelType model, byte order, StructureLearner.LearningType method)` This method finds the optimal structure of a model by using a given learning method (in some sense).
`static int[][]` | `getStructure(Tensor t, StructureLearner.ModelType model, byte order)` This method can be used to determine the optimal structure of a model.
`SymmetricTensor` | `getTensor(DataSet data, double[] weights, byte order, StructureLearner.LearningType method)` This method can be used to compute a `Tensor` that can be used to determine the optimal structure.
`void` | `setESS(double ess)` This method sets the ess (equivalent sample size) of the `StructureLearner`.

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

Constructor Detail

### StructureLearner

    public StructureLearner(AlphabetContainer con,
                            int length,
                            double ess)
                     throws IllegalArgumentException

  Creates a new `StructureLearner` for a given `AlphabetContainer`, a given length and a given equivalent sample size (ess).

  Parameters:
    `con` - the alphabets this instance should use
    `length` - the length
    `ess` - the ess (equivalent sample size, has to be non-negative)
  Throws:
    `IllegalArgumentException` - if the `AlphabetContainer` is not discrete, the length is not matching with the `AlphabetContainer` or the ess is below 0

### StructureLearner

    public StructureLearner(AlphabetContainer con,
                            int length)
                     throws IllegalArgumentException

  Creates a `StructureLearner` with equivalent sample size (ess) = 0.

  Parameters:
    `con` - the alphabets this instance should use
    `length` - the length
  Throws:
    `IllegalArgumentException` - if the `AlphabetContainer` is not discrete or the length is not matching with the `AlphabetContainer`
  See Also:
    `StructureLearner(AlphabetContainer, int, double)`

Method Detail

### getAlphabetContainer

    public AlphabetContainer getAlphabetContainer()

  This method returns the `AlphabetContainer` of the `StructureLearner`.

  Returns:
    the `AlphabetContainer` of the `StructureLearner`

### getEss

    public double getEss()

  This method returns the ess (equivalent sample size) of the `StructureLearner`.

  Returns:
    the ess of the `StructureLearner`

### setESS

    public void setESS(double ess)
                throws IllegalArgumentException

  This method sets the ess (equivalent sample size) of the `StructureLearner`.

  Parameters:
    `ess` - the ess of the `StructureLearner`
  Throws:
    `IllegalArgumentException` - if `ess < 0`

### getStructure

    public int[][] getStructure(DataSet data,
                                double[] weights,
                                StructureLearner.ModelType model,
                                byte order,
                                StructureLearner.LearningType method)
                         throws Exception

  This method finds the optimal structure of a model by using a given learning method (in some sense).

  Parameters:
    `data` - the `DataSet`
    `weights` - the weights
    `model` - the kind of model
    `order` - the Markov order
    `method` - the learning method
  Returns:
    the optimal structure of the specified model (in some sense)
  Throws:
    `Exception` - if something went wrong
  See Also:
    `getTensor(DataSet, double[], byte, LearningType)`, `getStructure(Tensor, ModelType, byte)`

### getStructure

    public static int[][] getStructure(Tensor t,
                                       StructureLearner.ModelType model,
                                       byte order)
                                throws Exception

  This method can be used to determine the optimal structure of a model.

  Parameters:
    `t` - the tensor containing all relevant weights (includes the learning method for the structure)
    `model` - the model type
    `order` - the model order
  Returns:
    the optimal structure of the specified model
  Throws:
    `Exception` - if something in the algorithm went wrong
  See Also:
    `getTensor(DataSet, double[], byte, LearningType)`

### getTensor

    public SymmetricTensor getTensor(DataSet data,
                                     double[] weights,
                                     byte order,
                                     StructureLearner.LearningType method)
                              throws IllegalArgumentException,
                                     WrongAlphabetException

  This method can be used to compute a `Tensor` that can be used to determine the optimal structure.

  Parameters:
    `data` - the data
    `weights` - the weights
    `order` - the Markov order
    `method` - the learning type
  Returns:
    a tensor containing all values the are necessary to determine the optimal structure (includes the learning method for the structure)
  Throws:
    `IllegalArgumentException` - if something is wrong with the given arguments
    `WrongAlphabetException` - if the `AlphabetContainer` of the data is not correct
  See Also:
    `getStructure(Tensor, ModelType, byte)`

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