calhoun.analysis.crf.features.tricycle13
Class PWM_evolution

java.lang.Object
  calhoun.analysis.crf.AbstractFeatureManager<CompositeInput>
      calhoun.analysis.crf.features.tricycle13.PWM_evolution

All Implemented Interfaces:

FeatureManager<CompositeInput>, FeatureManagerEdge<CompositeInput>, java.io.Serializable

public class PWM_evolution
extends AbstractFeatureManager<CompositeInput>
implements FeatureManagerEdge<CompositeInput>

See Also:

Serialized Form

Constructor Summary

PWM_evolution(java.util.List<int[]> geometry, java.util.List<int[]> dccorrection, java.util.List<int[]> markovhistory, java.util.List<int[]> clusters)

PWM_evolution(java.util.List<int[]> geometry, java.util.List<int[]> dccorrection, java.util.List<int[]> markovhistory, java.util.List<int[]> clusters, java.util.List<int[]> flags)

Method Summary

void	evaluateEdge(InputSequence<? extends CompositeInput> seq, int pos, int previousState, int state, FeatureList result) Evaluates the set of features managed by this object for the given arguments.
java.lang.String	getFeatureName(int featureIndex) Returns a human identifiable name for the feature referenced by a given index.
int	getNumFeatures() Returns the number of features maintained by this FeatureManager.
void	train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends CompositeInput>> data) Provides access to the entire training set so that FeatureManager can compute global properties and assign feature indices.

Methods inherited from class calhoun.analysis.crf.AbstractFeatureManager

getCacheStrategy, getInputComponent, setInputComponent

Methods inherited from class java.lang.Object

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

Methods inherited from interface calhoun.analysis.crf.FeatureManager

getCacheStrategy, getInputComponent, setInputComponent

Constructor Detail

PWM_evolution

public PWM_evolution(java.util.List<int[]> geometry,
                     java.util.List<int[]> dccorrection,
                     java.util.List<int[]> markovhistory,
                     java.util.List<int[]> clusters)

PWM_evolution

public PWM_evolution(java.util.List<int[]> geometry,
                     java.util.List<int[]> dccorrection,
                     java.util.List<int[]> markovhistory,
                     java.util.List<int[]> clusters,
                     java.util.List<int[]> flags)

Method Detail

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<CompositeInput>

Returns:

number of features managed by this FeatureManager

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<CompositeInput>

Parameters:

featureIndex - the index of this feature

Returns:

the human readable name of this feature

evaluateEdge

public void evaluateEdge(InputSequence<? extends CompositeInput> seq,
                         int pos,
                         int previousState,
                         int state,
                         FeatureList result)

Description copied from interface: FeatureManagerEdge

Evaluates the set of features managed by this object for the given arguments.

Specified by:

evaluateEdge in interface FeatureManagerEdge<CompositeInput>

train

public void train(int startingIndex,
                  ModelManager modelInfo,
                  java.util.List<? extends TrainingSequence<? extends CompositeInput>> 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<CompositeInput>

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