calhoun.analysis.crf.features.interval29
Class StateLengthLogprobInterval29

java.lang.Object
  calhoun.analysis.crf.features.interval29.StateLengthLogprobInterval29

All Implemented Interfaces:

FeatureManager<java.lang.Character>, FeatureManagerNodeExplicitLength<java.lang.Character>, java.io.Serializable

public class StateLengthLogprobInterval29
extends java.lang.Object
implements FeatureManagerNodeExplicitLength<java.lang.Character>

See Also:

Serialized Form

Constructor Summary

StateLengthLogprobInterval29()

Method Summary

void	evaluateNodeLength(InputSequence<? extends java.lang.Character> seq, int pos, int length, int state, FeatureList result)
CacheStrategySpec	getCacheStrategy() caching strategy that the CacheProcessor should use to cache values for this feature.
java.lang.String	getFeatureName(int featureIndex) Returns a human identifiable name for the feature referenced by a given index.
java.lang.String	getInputComponent() Specifies the particular component of the input which this feature manager uses, if the input data is a composite input which has multiple components.
int	getNumFeatures() Returns the number of features maintained by this FeatureManager.
boolean	isMultipleFeatures()
void	setExonExponential(boolean exonExponential)
void	setForceExponential(boolean forceExponential)
void	setInputComponent(java.lang.String name) Sets which particular component of a CompositeInput this FeatureManager has access to.
void	setMultipleFeatures(boolean multipleFeatures) set to true to indicate that intergenic, exonic, and intergenic lengths should each get a separate weight.
void	train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data) Provides access to the entire training set so that FeatureManager can compute global properties and assign feature indices.

Methods inherited from class java.lang.Object

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

Constructor Detail

StateLengthLogprobInterval29

public StateLengthLogprobInterval29()

Method Detail

setForceExponential

public void setForceExponential(boolean forceExponential)

setExonExponential

public void setExonExponential(boolean exonExponential)

evaluateNodeLength

public void evaluateNodeLength(InputSequence<? extends java.lang.Character> seq,
                               int pos,
                               int length,
                               int state,
                               FeatureList result)

Specified by:

evaluateNodeLength in interface FeatureManagerNodeExplicitLength<java.lang.Character>

getCacheStrategy

public CacheStrategySpec getCacheStrategy()

Description copied from interface: FeatureManager

caching strategy that the CacheProcessor should use to cache values for this feature. This is only a hint, the cache processor is not required to use this (or any) caching strategy. This base class defaults to the UNSPECIFIED cache strategy.

Specified by:

getCacheStrategy in interface FeatureManager<java.lang.Character>

Returns:

cache strategy specification appropriate for this feature.

getFeatureName

public java.lang.String getFeatureName(int featureIndex)

Description copied from interface: FeatureManager

Returns a human identifiable name for the feature referenced by a given index. Used for display purposes only.

Specified by:

getFeatureName in interface FeatureManager<java.lang.Character>

Parameters:

featureIndex - the index of this feature

Returns:

the human readable name of this feature

getInputComponent

public java.lang.String getInputComponent()

Description copied from interface: FeatureManager

Specifies the particular component of the input which this feature manager uses, if the input data is a composite input which has multiple components. May be null if the input is not a composite or if the feature manager has access to all of the input. See the Conrad User's Guide for how to set up input data.

Specified by:

getInputComponent in interface FeatureManager<java.lang.Character>

Returns:

string name of the input component this feature should look at, or null if the feature has access to all inputs.

setInputComponent

public void setInputComponent(java.lang.String name)

Description copied from interface: FeatureManager

Sets which particular component of a CompositeInput this FeatureManager has access to.

Specified by:

setInputComponent in interface FeatureManager<java.lang.Character>

Parameters:

name - name of the component of the CompositeInput this FeatureManager has access to. Usually set automatically during configuration.

train

public void train(int startingIndex,
                  ModelManager modelInfo,
                  java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)

Description copied from interface: FeatureManager

Provides access to the entire training set so that FeatureManager can compute global properties and assign feature indices. This will be called before any evaluations are requested. If the FeatureManager can have a variable number of features, this must be fixed within this method.

Specified by:

train in interface FeatureManager<java.lang.Character>

Parameters:

startingIndex - the feature index of the first feature owned by this FeatureManager. Each FeatureManager must use up consecutive indexes, so the last index used will be startingIndex + numFeatures - 1.

modelInfo - the model that contains this feature

data - the full list of training sequences to use to train the feature

getNumFeatures

public int getNumFeatures()

Description copied from interface: FeatureManager

Returns the number of features maintained by this FeatureManager. This number must be fixed after the call to trainFeatures is complete.

Specified by:

getNumFeatures in interface FeatureManager<java.lang.Character>

Returns:

number of features managed by this FeatureManager

isMultipleFeatures

public boolean isMultipleFeatures()

Returns:

Returns the multipleFeatures.

setMultipleFeatures

public void setMultipleFeatures(boolean multipleFeatures)

set to true to indicate that intergenic, exonic, and intergenic lengths should each get a separate weight.

Parameters:

multipleFeatures - The multipleFeatures to set.