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java.lang.Objectde.jstacs.models.AbstractModel
de.jstacs.models.UniformModel
public class UniformModel
This class represents an uniform model. Sometimes it's also called uninformed model. It can be used if nothing is known about a statistical process.
Field Summary |
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Fields inherited from class de.jstacs.models.AbstractModel |
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alphabets, length |
Constructor Summary | |
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UniformModel(AlphabetContainer alphabet)
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UniformModel(StringBuffer stringBuff)
The constructor for the Storable interface. |
Method Summary | |
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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 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 getCharacteristsics . |
double |
getProbFor(Sequence sequence,
int startpos,
int endpos)
Returns the probability of the given sequence given the model. |
boolean |
isTrained()
Returns true . |
String |
toString()
Returns the String "". |
StringBuffer |
toXML()
This method returns an XML-representation of an instance of the implementing class. |
void |
train(Sample data,
double[] weights)
Deprecated. |
Methods inherited from class de.jstacs.models.AbstractModel |
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getAlphabetContainer, getCharacteristics, getLength, getLogProbFor, getLogProbFor, getLogProbFor, getLogProbFor, getLogProbFor, getPriorTerm, getProbFor, getProbFor, set, setNewAlphabetContainerInstance, train |
Methods inherited from class java.lang.Object |
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equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait |
Constructor Detail |
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public UniformModel(AlphabetContainer alphabet)
alphabet
- public UniformModel(StringBuffer stringBuff) throws NonParsableException
Storable
interface.
stringBuff
- the StringBuffer
NonParsableException
- if the StringBuffer is not parsableMethod Detail |
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public UniformModel clone() throws CloneNotSupportedException
AbstractModel
Object
's clone-method.
clone
in interface Model
clone
in class AbstractModel
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: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
CloneNotSupportedException
public double getProbFor(Sequence sequence, int startpos, int endpos) throws IllegalArgumentException, WrongAlphabetException
Model
startpos
to endpos
(inclusive) should be returned.
length
and alphabets
define the type of data that can be modeled and therefore
both has to be checked.
sequence
- the sequencestartpos
- the start positionendpos
- the last position to be taken into account
IllegalArgumentException
WrongAlphabetException
public boolean isTrained()
true
.
true
public void fromXML(StringBuffer representation) throws NonParsableException
AbstractModel
toXML()
.
fromXML
in class AbstractModel
representation
- the representation
NonParsableException
- if the StringBuffer is not parsable or the representation is conflictingAbstractModel.AbstractModel(StringBuffer)
public StringBuffer toXML()
Storable
public String toString()
toString
in interface Model
toString
in class Object
public void train(Sample data, double[] weights) throws IOException
data
- the given sequencesweights
- the weights of the elements, each weight should be non-negative
IOException
Sample.getElementAt(int)
,
Sample.ElementEnumerator
public Sample emitSample(int n, int... lengths) throws Exception
Model
emitSample( int n, int l )
should return a sample with n
sequences of length l
.
emitSample( int n, int[] l )
should return a sample with n
sequences which have a sequence length corresponding to the entry in the array
emitSample( int n )
and emitSample( int n, null )
should return a sample with
n
sequences of length of the model (Model.getLength()
)
emitSample
in interface Model
emitSample
in class AbstractModel
n
- the number of sequences that should be contained in the returned samplelengths
- the length of the sequences for a homogeneous model; for an inhomogeneous model this parameter should be
null
or an array of size 0.
Exception
- an Exception should be thrown if the emission did not succeed.
NotTrainedException
- a NotTrainedException should be thrown if the model is not trained yet.Sample
public double getLogPriorTerm() throws Exception
Model
Exception
- if something went wrongModel.getPriorTerm()
public byte getMaximalMarkovOrder() throws UnsupportedOperationException
Model
getMaximalMarkovOrder
in interface Model
getMaximalMarkovOrder
in class AbstractModel
UnsupportedOperationException
- if the model can't give a proper answerpublic NumericalResultSet getNumericalCharacteristics() throws Exception
Model
getCharacteristsics
.
Exception
- an Exception
is thrown if some of the characteristics could not be definedpublic String getInstanceName()
Model
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