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 maximum likelihood (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