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## de.jstacs.models
Class UniformModel

    java.lang.Object
      de.jstacs.models.AbstractModel
          de.jstacs.models.UniformModel

All Implemented Interfaces:
  Model, Storable, Cloneable

    public class UniformModel

extends AbstractModel

This class represents a uniform model. Sometimes it's also called uninformed model. It can be used if nothing is known about a statistical process.

Author:
  Jens Keilwagen

Field Summary

Fields inherited from class de.jstacs.models.AbstractModel
`alphabets, length`

Constructor Summary
`UniformModel(AlphabetContainer alphabet)` Creates a new `UniformModel` using a given `AlphabetContainer`.
`UniformModel(StringBuffer stringBuff)` The standard constructor for the interface `Storable`.

Method Summary
`UniformModel` | `clone()` Follows the conventions of `Object`'s `clone()`-method.
`Sample` | `emitSample(int n, int... lengths)` This method returns a `Sample` object containing artificial sequence(s).
`void` | `fromXML(StringBuffer representation)` This method should only be used by the constructor that works on a `StringBuffer`.
`String` | `getInstanceName()` Should return a short instance name such as iMM(0), BN(2), ...
`double` | `getLogPriorTerm()` Returns a value that is proportional to the log of the prior.
`byte` | `getMaximalMarkovOrder()` This method returns the maximal used Markov order, if possible.
`NumericalResultSet` | `getNumericalCharacteristics()` Returns the subset of numerical values that are also returned by `Model.getCharacteristics()`.
`double` | `getProbFor(Sequence sequence, int startpos, int endpos)` Returns the probability of (a part of) the given sequence given the model.
`boolean` | `isTrained()` Returns `true` if the model is trained, `false` otherwise.
`String` | `toString()` Returns the String "".
`StringBuffer` | `toXML()` This method returns an XML representation as `StringBuffer` of an instance of the implementing class.
`void` | `train(Sample data, double[] weights)` Deprecated.

Methods inherited from class de.jstacs.models.AbstractModel
`getAlphabetContainer, getCharacteristics, getLength, getLogProbFor, getLogProbFor, getLogProbFor, getLogProbFor, getLogProbFor, getPriorTerm, getProbFor, getProbFor, set, setNewAlphabetContainerInstance, train`

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

Constructor Detail

### UniformModel

    public UniformModel(AlphabetContainer alphabet)

  Creates a new `UniformModel` using a given `AlphabetContainer`.

  Parameters:
    `alphabet` - the alphabets used in the model

### UniformModel

    public UniformModel(StringBuffer stringBuff)
                 throws NonParsableException

  The standard constructor for the interface `Storable`. Creates a new `UniformModel` out of a `StringBuffer`.

  Parameters:
    `stringBuff` - the `StringBuffer` to be parsed
  Throws:
    `NonParsableException` - if the `StringBuffer` is not parsable

Method Detail

### clone

    public UniformModel clone()
                       throws CloneNotSupportedException

  Description copied from class: `AbstractModel`
  Follows the conventions of `Object`'s `clone()`-method.

  Specified by:
    `clone` in interface `Model`
  Overrides:
    `clone` in class `AbstractModel`
  Returns:
    an object, that is a copy of the current `AbstractModel` (the member-`AlphabetContainer` isn't deeply cloned since it is assumed to be immutable). The type of the returned object is defined by the class `X` directly inherited from `AbstractModel`. Hence `X`'s `clone()`-method should work as:
    1. `Object o = (X)super.clone();`
    2. all additional member variables of `o` defined by `X` that are not of simple data-types like `int`, `double`, ... have to be deeply copied
    3. `return o`
  Throws:
    `CloneNotSupportedException` - if something went wrong while cloning

### getProbFor

    public double getProbFor(Sequence sequence,
                             int startpos,
                             int endpos)
                      throws IllegalArgumentException,
                             WrongAlphabetException

  Description copied from interface: `Model`
  Returns 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 `Model.getProbFor(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.

  Parameters:
    `sequence` - the given sequence
    `startpos` - the start position within the given sequence
    `endpos` - the last position to be taken into account
  Returns:
    the probability or the value of the density function of (the part of) the given sequence given the model
  Throws:
    `IllegalArgumentException`
    `WrongAlphabetException`

### isTrained

    public boolean isTrained()

  Returns `true` if the model is trained, `false` otherwise.

  Returns:
    `true` if the model is trained, `false` otherwise

### fromXML

    public void fromXML(StringBuffer representation)
                 throws NonParsableException

  Description copied from class: `AbstractModel`
  This method should only be used by the constructor that works on a `StringBuffer`. It is the counter part of `Storable.toXML()`.

  Specified by:
    `fromXML` in class `AbstractModel`
  Parameters:
    `representation` - the XML representation of the model
  Throws:
    `NonParsableException` - if the `StringBuffer` is not parsable or the representation is conflicting
  See Also:
    `AbstractModel.AbstractModel(StringBuffer)`

### toXML

    public StringBuffer toXML()

  Description copied from interface: `Storable`
  This method returns an XML representation as `StringBuffer` of an instance of the implementing class.

  Returns:
    the XML representation

### toString

    public String toString()

  Returns the String "".

  Specified by:
    `toString` in interface `Model`
  Overrides:
    `toString` in class `Object`
  Returns:
    the representation as `String`

### train

    @Deprecated
    public void train(Sample data,
                                 double[] weights)
               throws IOException

  Deprecated.

  Description copied from interface: `Model`
  Trains the `Model` object given the data as `Sample` using the specified weights. The weight at position i belongs to the element at position i. So the array `weight` should have the number of sequences in the sample as dimension. (Optionally it is possible to use `weight == null` if all weights have the value one.)
  This method should work non-incrementally. That means the result of the following series: `train(data1)`; `train(data2)` should be a fully trained model over `data2` and not over `data1+data2`. All parameters of the model were given by the call of the constructor.

  Parameters:
    `data` - the given sequences as `Sample`
    `weights` - the weights of the elements, each weight should be non-negative
  Throws:
    `IOException`
  See Also:
    `Sample.getElementAt(int)`, `Sample.ElementEnumerator`

### emitSample

    public Sample emitSample(int n,
                             int... lengths)
                      throws Exception

  Description copied from interface: `Model`
  This method returns a `Sample` object containing artificial sequence(s).
  There are two different possibilities to create a sample for a model with length 0 (homogeneous models).
  1. `emitSample( int n, int l )` should return a sample with `n` sequences of length `l`.
  2. `emitSample( int n, int[] l )` should return a sample 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 sample for a model with length greater than 0 (inhomogeneous models).
  `emitSample( int n )` and `emitSample( int n, null )` should return a sample with `n` sequences of length of the model ( `Model.getLength()`).
  The standard implementation throws an `Exception`.

  Specified by:
    `emitSample` in interface `Model`
  Overrides:
    `emitSample` in class `AbstractModel`
  Parameters:
    `n` - the number of sequences that should be contained in the returned sample
    `lengths` - 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 `Sample` containing the artificial sequence(s)
  Throws:
    `Exception` - if the emission did not succeed
    `NotTrainedException` - if the model is not trained yet
  See Also:
    `Sample`

### getLogPriorTerm

    public double getLogPriorTerm()
                           throws Exception

  Description copied from interface: `Model`
  Returns a value that is proportional to the log of the prior. For ML 0 should be returned.

  Returns:
    a value that is proportional to the log of the prior
  Throws:
    `Exception` - if something went wrong
  See Also:
    `Model.getPriorTerm()`

### getMaximalMarkovOrder

    public byte getMaximalMarkovOrder()
                               throws UnsupportedOperationException

  Description copied from interface: `Model`
  This method returns the maximal used Markov order, if possible.

  Specified by:
    `getMaximalMarkovOrder` in interface `Model`
  Overrides:
    `getMaximalMarkovOrder` in class `AbstractModel`
  Returns:
    maximal used Markov order
  Throws:
    `UnsupportedOperationException` - if the model can't give a proper answer

### getNumericalCharacteristics

    public NumericalResultSet getNumericalCharacteristics()
                                                   throws Exception

  Description copied from interface: `Model`
  Returns the subset of numerical values that are also returned by `Model.getCharacteristics()`.

  Returns:
    the numerical characteristics of the current instance of the model
  Throws:
    `Exception` - if some of the characteristics could not be defined

### getInstanceName

    public String getInstanceName()

  Description copied from interface: `Model`
  Should return a short instance name such as iMM(0), BN(2), ...

  Returns:
    a short instance name

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