com.aliasi.lm
Class UniformProcessLM

java.lang.Object
  com.aliasi.lm.UniformProcessLM

All Implemented Interfaces:

Handler, ObjectHandler<CharSequence>, LanguageModel, LanguageModel.Dynamic, LanguageModel.Process, Compilable

public class UniformProcessLM
extends Object
implements LanguageModel.Dynamic, LanguageModel.Process

A UniformLM.Sequence implements a uniform sequence language model with a specified number of outcomes and the same probability assigned to the end-of-stream marker. The formula for computing sequence likelihood estimates is:

log2Estimate(cSeq) = = log2 ( (cSeq.length()+1) / (numOutcomes+1) )

Adding one to the number of outcomes makes the end-of-sequence just as likely as any other character. Adding one to the sequence length adds the log likelihood of the end-of-sequence marker itself.

Since:

LingPipe2.0

Version:

4.1.0

Author:

Bob Carpenter

Nested Class Summary

Nested classes/interfaces inherited from interface com.aliasi.lm.LanguageModel

LanguageModel.Conditional, LanguageModel.Dynamic, LanguageModel.Process, LanguageModel.Sequence, LanguageModel.Tokenized

Constructor Summary

UniformProcessLM()
Construct a uniform process language model with a number of outcomes equal to the total number of characters.

UniformProcessLM(double crossEntropyRate)
Construct a uniform process language model with the specified character cross-entropy rate.

UniformProcessLM(int numOutcomes)
Construct a uniform process language model with the specified number of outcomes.

Method Summary

void	compileTo(ObjectOutput objOut) Writes a compiled version of this model to the specified object output.
void	handle(CharSequence cs) This method for training a character sequence is supplied for compatibility with the dynamic language model interface, but is implemented to do nothing.
double	log2Estimate(char[] cs, int start, int end) Returns an estimate of the log (base 2) probability of the specified character slice.
double	log2Estimate(CharSequence cSeq) Returns an estimate of the log (base 2) probability of the specified character sequence.
int	numOutcomes() Returns the number of outcomes for this uniform model.
void	train(char[] cs, int start, int end) Ignores the training data.
void	train(char[] cs, int start, int end, int count) Ignores the training data.
void	train(CharSequence cSeq) Ignores the training data.
void	train(CharSequence cSeq, int count) Ignores the training data.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

UniformProcessLM

public UniformProcessLM()

Construct a uniform process language model with a number of outcomes equal to the total number of characters.

UniformProcessLM

public UniformProcessLM(int numOutcomes)

Construct a uniform process language model with the specified number of outcomes. The per-character conditional estimate is 1/numOutcomes.

Parameters:

numOutcomes - The number of outcomes for this language model.

UniformProcessLM

public UniformProcessLM(double crossEntropyRate)

Construct a uniform process language model with the specified character cross-entropy rate. Recall that cross-entropy is the negative character average log probability:

log2 P(cs) = - crossEntropyRate * cs.length()

The number of outcomes is set by rounding down the exponent of the cross-entropy:

numOutcomes = (int) 2.0crossEntropyRate

Parameters:

crossEntropyRate - Character cross-entropy rate of the uniform model.

Method Detail

numOutcomes

public int numOutcomes()

Returns the number of outcomes for this uniform model.

Returns:

The number of outcomes for this uniform model.

compileTo

public void compileTo(ObjectOutput objOut)
               throws IOException

Writes a compiled version of this model to the specified object output. The object read back in will also be an instance of UniformProcessLM.

Specified by:

compileTo in interface Compilable

Parameters:

objOut - Object output to which this model is written.

Throws:

IOException - If there is an I/O error during the write.

handle

public void handle(CharSequence cs)

This method for training a character sequence is supplied for compatibility with the dynamic language model interface, but is implemented to do nothing.

Specified by:

handle in interface ObjectHandler<CharSequence>

Parameters:

cs - Ignored.

train

public void train(char[] cs,
                  int start,
                  int end)

Ignores the training data.

Specified by:

train in interface LanguageModel.Dynamic

Parameters:

cs - Ignored.

start - Ignored.

end - Ignored.

train

public void train(char[] cs,
                  int start,
                  int end,
                  int count)

Ignores the training data.

Specified by:

train in interface LanguageModel.Dynamic

Parameters:

cs - Ignored.

start - Ignored.

end - Ignored.

count - Ignored.

train

public void train(CharSequence cSeq)

Ignores the training data.

Specified by:

train in interface LanguageModel.Dynamic

Parameters:

cSeq - Ignored.

train

public void train(CharSequence cSeq,
                  int count)

Ignores the training data.

Specified by:

train in interface LanguageModel.Dynamic

Parameters:

cSeq - Ignored.

count - Ignored.

log2Estimate

public double log2Estimate(char[] cs,
                           int start,
                           int end)

Description copied from interface: LanguageModel

Returns an estimate of the log (base 2) probability of the specified character slice.

Specified by:

log2Estimate in interface LanguageModel

Parameters:

cs - Underlying array of characters.

start - Index of first character in slice.

end - One plus index of last character in slice.

Returns:

Log estimate of likelihood of specified character sequence.

log2Estimate

public double log2Estimate(CharSequence cSeq)

Description copied from interface: LanguageModel

Returns an estimate of the log (base 2) probability of the specified character sequence.

Specified by:

log2Estimate in interface LanguageModel

Parameters:

cSeq - Character sequence to estimate.

Returns:

Log estimate of likelihood of specified character sequence.