Uses of Class
edu.illinois.cs.cogcomp.lbjava.classify.Feature

Packages that use Feature

Package	Description
edu.illinois.cs.cogcomp.lbjava.classify
edu.illinois.cs.cogcomp.lbjava.infer
edu.illinois.cs.cogcomp.lbjava.learn
edu.illinois.cs.cogcomp.lbjava.util

Uses of Feature in edu.illinois.cs.cogcomp.lbjava.classify

Subclasses of Feature in edu.illinois.cs.cogcomp.lbjava.classify

Modifier and Type	Class and Description
class	DiscreteArrayFeature A discrete array feature keeps track of its index in the classifier's returned array as well as the total number of features in that array.
class	DiscreteArrayStringFeature A discrete array feature keeps track of its index in the classifier's returned array as well as the total number of features in that array.
class	DiscreteConjunctiveFeature Represents the conjunction of two discrete features.
class	DiscreteFeature A discrete feature takes on one value from a set of discontinuous values.
class	DiscretePrimitiveFeature A primitive discrete feature is a discrete feature with a string value.
class	DiscretePrimitiveStringFeature This feature is functionally equivalent to DiscretePrimitiveFeature, however its DiscretePrimitiveStringFeature.value is stored as a String instead of a ByteString.
class	DiscreteReferrer A referring discrete feature is one that has its own identifier, but whose value comes from a separate feature that it refers to.
class	DiscreteReferringFeature A referring discrete feature is one that has its own identifier, but whose value comes from a separate feature that it refers to.
class	DiscreteReferringStringFeature A referring discrete feature is one that has its own identifier, but whose value comes from a separate feature that it refers to.
class	RealArrayFeature A real array feature keeps track of its index in the classifier's returned array.
class	RealArrayStringFeature A real array feature keeps track of its index in the classifier's returned array.
class	RealConjunctiveFeature Represents the conjunction of two features.
class	RealFeature A real feature takes on any value representable by a double.
class	RealPrimitiveFeature A real feature takes on any value representable by a double.
class	RealPrimitiveStringFeature A real feature takes on any value representable by a double.
class	RealReferrer A referring real feature is one that has its own identifier, but whose value comes from a separate feature that it refers to.
class	RealReferringFeature A referring real feature is one that has its own identifier, but whose value comes from a separate feature that it refers to.
class	RealReferringStringFeature A referring real feature is one that has its own identifier, but whose value comes from a separate feature that it refers to.

Fields in edu.illinois.cs.cogcomp.lbjava.classify declared as Feature

Modifier and Type	Field and Description
protected Feature	RealConjunctiveFeature.left One feature argument.
protected Feature	RealConjunctiveFeature.right The other feature argument.

Methods in edu.illinois.cs.cogcomp.lbjava.classify that return Feature

Modifier and Type	Method and Description
Feature	RealFeature.conjunction(Feature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
abstract Feature	Feature.conjunction(Feature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
Feature	DiscreteFeature.conjunction(Feature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	Feature.conjunctWith(DiscreteFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	DiscreteFeature.conjunctWith(DiscreteFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	Feature.conjunctWith(RealFeature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
Feature	RealReferringFeature.encode(String e) Returns a feature object in which any strings that are being used to represent an identifier or value have been encoded in byte strings.
Feature	RealPrimitiveStringFeature.encode(String e) Returns a feature object in which any strings that are being used to represent an identifier or value have been encoded in byte strings.
Feature	RealConjunctiveFeature.encode(String e) Returns a feature object in which any strings that are being used to represent an identifier or value have been encoded in byte strings.
abstract Feature	Feature.encode(String e) Returns a feature object in which any strings that are being used to represent an identifier or value have been encoded in byte strings.
Feature	DiscreteReferringFeature.encode(String e) Returns a feature object in which any strings that are being used to represent an identifier or value have been encoded in byte strings.
Feature	DiscretePrimitiveStringFeature.encode(String e) Returns a feature object in which any strings that are being used to represent an identifier or value have been encoded in byte strings.
Feature	DiscreteConjunctiveFeature.encode(String e) Returns a feature object in which any strings that are being used to represent an identifier or value have been encoded in byte strings.
Feature	RealReferringStringFeature.encode(String e) Returns a feature object in which any strings that are being used to represent an identifier or value have been encoded in byte strings.
Feature	RealPrimitiveFeature.encode(String e) Returns a feature object in which any strings that are being used to represent an identifier or value have been encoded in byte strings.
Feature	RealArrayStringFeature.encode(String e) Returns a feature object in which any strings that are being used to represent an identifier or value have been encoded in byte strings.
Feature	DiscreteReferringStringFeature.encode(String e) Returns a feature object in which any strings that are being used to represent an identifier or value have been encoded in byte strings.
Feature	DiscretePrimitiveFeature.encode(String e) Returns a feature object in which any strings that are being used to represent an identifier or value have been encoded in byte strings.
Feature	DiscreteArrayStringFeature.encode(String e) Returns a feature object in which any strings that are being used to represent an identifier or value have been encoded in byte strings.
Feature	Classifier.featureValue(Object o) Returns the classification of the given example object as a single feature instead of a FeatureVector.
Feature	ValueComparer.featureValue(Object o) Returns the classification of the given example object as a single feature instead of a FeatureVector.
Feature	MultiValueComparer.featureValue(Object o) Returns the classification of the given example object as a single feature instead of a FeatureVector.
Feature	FeatureVector.firstFeature() Returns the first feature in FeatureVector.features.
Feature	FeatureVector.firstLabel() Returns the first feature in FeatureVector.labels.
protected Feature	RealConjunctiveFeature.getArgumentKey(Feature f, Lexicon lexicon, boolean training, int label) A helper method for RealConjunctiveFeature.getFeatureKey(Lexicon,boolean,int), this method computes the feature keys corresponding to the arguments of the conjunction.
Feature	FeatureVector.getFeature(int index) Returns the feature at the specified index.
Feature	Feature.getFeatureKey(Lexicon lexicon) Return the feature that should be used to index this feature into a lexicon.
Feature	RealReferringFeature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
Feature	RealPrimitiveStringFeature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
Feature	RealConjunctiveFeature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
Feature	RealArrayFeature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
abstract Feature	Feature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
Feature	DiscreteReferringFeature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
Feature	DiscretePrimitiveStringFeature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
Feature	DiscreteConjunctiveFeature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
Feature	DiscreteArrayFeature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
Feature	RealReferringStringFeature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
Feature	RealPrimitiveFeature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
Feature	RealArrayStringFeature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
Feature	DiscreteReferringStringFeature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
Feature	DiscretePrimitiveFeature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
Feature	DiscreteArrayStringFeature.getFeatureKey(Lexicon lexicon, boolean training, int label) Return the feature that should be used to index this feature into a lexicon.
Feature	FeatureVector.getLabel(int index) Returns the label at the specified index.
Feature	RealConjunctiveFeature.getLeft() Returns the value of RealConjunctiveFeature.left.
Feature	RealConjunctiveFeature.getRight() Returns the value of RealConjunctiveFeature.right.
static Feature	Feature.lexReadFeature(edu.illinois.cs.cogcomp.core.datastructures.vectors.ExceptionlessInputStream in, Lexicon lex, Class c, String p, String g, String si, ByteString bi) Reads the representation of a feature of any type as stored by a lexicon, omitting redundant information.
static Feature	Feature.readFeature(edu.illinois.cs.cogcomp.core.datastructures.vectors.ExceptionlessInputStream in) Reads the binary representation of a feature of any type from the given stream.
Feature	RealReferringFeature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.
Feature	RealPrimitiveStringFeature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.
Feature	RealConjunctiveFeature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.
Feature	RealArrayFeature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.
abstract Feature	Feature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.
Feature	DiscreteReferringFeature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.
Feature	DiscretePrimitiveStringFeature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.
Feature	DiscreteConjunctiveFeature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.
Feature	DiscreteArrayFeature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.
Feature	RealReferringStringFeature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.
Feature	RealPrimitiveFeature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.
Feature	RealArrayStringFeature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.
Feature	DiscreteReferringStringFeature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.
Feature	DiscretePrimitiveFeature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.
Feature	DiscreteArrayStringFeature.withStrength(double s) Returns a new feature object that's identical to this feature except its strength is given by s.

Methods in edu.illinois.cs.cogcomp.lbjava.classify with parameters of type Feature

Modifier and Type	Method and Description
void	FeatureVector.addFeature(Feature f) Adds a feature to the vector.
void	FeatureVector.addLabel(Feature l) Adds a label to the vector.
boolean	RealReferringFeature.classEquivalent(Feature f) Some features are functionally equivalent, differing only in the encoding of their values; this method will return true iff the class of this feature and f are different, but they differ only because they encode their values differently.
boolean	RealPrimitiveStringFeature.classEquivalent(Feature f) Some features are functionally equivalent, differing only in the encoding of their values; this method will return true iff the class of this feature and f are different, but they differ only because they encode their values differently.
boolean	RealArrayFeature.classEquivalent(Feature f) Some features are functionally equivalent, differing only in the encoding of their values; this method will return true iff the class of this feature and f are different, but they differ only because they encode their values differently.
boolean	Feature.classEquivalent(Feature f) Some features are functionally equivalent, differing only in the encoding of their values; this method will return true iff the class of this feature and f are different, but they differ only because they encode their values differently.
boolean	DiscreteReferringFeature.classEquivalent(Feature f) Some features are functionally equivalent, differing only in the encoding of their values; this method will return true iff the class of this feature and f are different, but they differ only because they encode their values differently.
boolean	DiscretePrimitiveStringFeature.classEquivalent(Feature f) Some features are functionally equivalent, differing only in the encoding of their values; this method will return true iff the class of this feature and f are different, but they differ only because they encode their values differently.
boolean	DiscreteArrayFeature.classEquivalent(Feature f) Some features are functionally equivalent, differing only in the encoding of their values; this method will return true iff the class of this feature and f are different, but they differ only because they encode their values differently.
boolean	RealReferringStringFeature.classEquivalent(Feature f) Some features are functionally equivalent, differing only in the encoding of their values; this method will return true iff the class of this feature and f are different, but they differ only because they encode their values differently.
boolean	RealPrimitiveFeature.classEquivalent(Feature f) Some features are functionally equivalent, differing only in the encoding of their values; this method will return true iff the class of this feature and f are different, but they differ only because they encode their values differently.
boolean	RealArrayStringFeature.classEquivalent(Feature f) Some features are functionally equivalent, differing only in the encoding of their values; this method will return true iff the class of this feature and f are different, but they differ only because they encode their values differently.
boolean	DiscreteReferringStringFeature.classEquivalent(Feature f) Some features are functionally equivalent, differing only in the encoding of their values; this method will return true iff the class of this feature and f are different, but they differ only because they encode their values differently.
boolean	DiscretePrimitiveFeature.classEquivalent(Feature f) Some features are functionally equivalent, differing only in the encoding of their values; this method will return true iff the class of this feature and f are different, but they differ only because they encode their values differently.
boolean	DiscreteArrayStringFeature.classEquivalent(Feature f) Some features are functionally equivalent, differing only in the encoding of their values; this method will return true iff the class of this feature and f are different, but they differ only because they encode their values differently.
Feature	RealFeature.conjunction(Feature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
abstract Feature	Feature.conjunction(Feature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
Feature	DiscreteFeature.conjunction(Feature f, Classifier c) Create a feature representing the conjunction of this feature with the given argument feature.
protected Feature	RealConjunctiveFeature.getArgumentKey(Feature f, Lexicon lexicon, boolean training, int label) A helper method for RealConjunctiveFeature.getFeatureKey(Lexicon,boolean,int), this method computes the feature keys corresponding to the arguments of the conjunction.
protected DiscreteFeature	DiscreteConjunctiveFeature.getArgumentKey(Feature f, Lexicon lexicon, int label) A helper method for DiscreteConjunctiveFeature.getFeatureKey(Lexicon,boolean,int), this method computes the feature keys corresponding to the arguments of the conjunction.

Constructors in edu.illinois.cs.cogcomp.lbjava.classify with parameters of type Feature

Constructor and Description
FeatureVector(Feature f) Creates the vector and adds the given feature to it.
FeatureVector(Feature[] features) Creates the vector and adds the given features to it.
RealConjunctiveFeature(Classifier c, Feature l, Feature r) Creates a new conjunctive feature taking the package and name of the given classifier.
RealConjunctiveFeature(String p, String c, Feature l, Feature r) Creates a new conjunctive feature.

Uses of Feature in edu.illinois.cs.cogcomp.lbjava.infer

Methods in edu.illinois.cs.cogcomp.lbjava.infer that return Feature

Modifier and Type	Method and Description
Feature	ParameterizedConstraint.featureValue(Object o) Returns the classification of the given example object as a single feature instead of a FeatureVector.

Uses of Feature in edu.illinois.cs.cogcomp.lbjava.learn

Fields in edu.illinois.cs.cogcomp.lbjava.learn declared as Feature

Modifier and Type	Field and Description
protected Feature	MuxLearner.defaultFeature A feature whose value is MuxLearner.defaultPrediction.

Methods in edu.illinois.cs.cogcomp.lbjava.learn that return Feature

Modifier and Type	Method and Description
protected Feature	SparseMIRA.conjunctiveValueOf(int[] exampleFeatures, double[] exampleValues, Iterator I) This method is a surrogate for SparseMIRA.valueOf(int[],double[],Collection) when the labeler is known to produce conjunctive features.
protected Feature	SupportVectorMachine.conjunctiveValueOf(int[] exampleFeatures, double[] exampleValues, Iterator I) This method is a surrogate for SupportVectorMachine.valueOf(int[],double[],Collection) when the labeler is known to produce conjunctive features.
protected Feature	SparseNetworkLearner.conjunctiveValueOf(int[] exampleFeatures, double[] exampleValues, Iterator I) This method is a surrogate for SparseNetworkLearner.valueOf(int[],double[],Collection) when the labeler is known to produce conjunctive features.
Feature	Learner.featureValue(FeatureVector vector) Returns the classification of the given feature vector as a single feature instead of a FeatureVector.
Feature	SparseMIRA.featureValue(int[] f, double[] v) Returns the classification of the given example as a single feature instead of a FeatureVector.
Feature	MuxLearner.featureValue(int[] f, double[] v) Returns the classification of the given example as a single feature instead of a FeatureVector.
Feature	SupportVectorMachine.featureValue(int[] f, double[] v) Returns the classification of the given example as a single feature instead of a FeatureVector.
Feature	SparseNetworkLearner.featureValue(int[] f, double[] v) Returns the classification of the given example as a single feature instead of a FeatureVector.
Feature	AdaGrad.featureValue(int[] f, double[] v) Returns the classification of the given example as a single feature instead of a FeatureVector.
Feature	NaiveBayes.featureValue(int[] f, double[] v) Returns the classification of the given example as a single feature instead of a FeatureVector.
Feature	StochasticGradientDescent.featureValue(int[] f, double[] v) Returns the classification of the given example as a single feature instead of a FeatureVector.
Feature	LinearThresholdUnit.featureValue(int[] f, double[] v) Returns the classification of the given example as a single feature instead of a FeatureVector.
Feature	Learner.featureValue(int[] f, double[] v) Returns the classification of the given example as a single feature instead of a FeatureVector.
Feature	AdaBoost.featureValue(int[] exampleFeatures, double[] exampleValues) Returns the classification of the given example as a single feature instead of a FeatureVector.
Feature	Learner.featureValue(Object example) Returns the classification of the given example object as a single feature instead of a FeatureVector.
Feature	Lexicon.getChildFeature(Feature f, int label) Used to lookup the children of conjunctive and referring features during training, this method checks Lexicon.lexiconChildren if the feature isn't present in Lexicon.lexicon and Lexicon.lexiconInv, and then stores the given feature in Lexicon.lexiconChildren if it wasn't present anywhere.
Feature	ChildLexicon.getChildFeature(Feature f, int label) This method adds the given feature to this lexicon and also recursively adds its children, if any.
Feature	Lexicon.lookupKey(int i) Does a reverse lexicon lookup and returns the Feature associated with the given integer key, and null if no such feature exists.
Feature	ChildLexicon.remove(Feature f) Removes the mapping for the given feature from this lexicon and returns the feature object representing it that was stored here.
Feature	SparseMIRA.valueOf(int[] exampleFeatures, double[] exampleValues, Collection candidates) Using this method, the winner-take-all competition is narrowed to involve only those labels contained in the specified list.
Feature	SupportVectorMachine.valueOf(int[] exampleFeatures, double[] exampleValues, Collection candidates) Using this method, the winner-take-all competition is narrowed to involve only those labels contained in the specified list.
Feature	SparseNetworkLearner.valueOf(int[] exampleFeatures, double[] exampleValues, Collection candidates) Using this method, the winner-take-all competition is narrowed to involve only those labels contained in the specified list.
Feature	SparseMIRA.valueOf(Object example, Collection candidates) Using this method, the winner-take-all competition is narrowed to involve only those labels contained in the specified list.
Feature	SupportVectorMachine.valueOf(Object example, Collection candidates) Using this method, the winner-take-all competition is narrowed to involve only those labels contained in the specified list.
Feature	SparseNetworkLearner.valueOf(Object example, Collection candidates) Using this method, the winner-take-all competition is narrowed to involve only those labels contained in the specified list.

Methods in edu.illinois.cs.cogcomp.lbjava.learn with parameters of type Feature

Modifier and Type	Method and Description
int	ChildLexicon.childLexiconLookup(Feature f, int label) Updates the counts in ChildLexicon.parents for the children of f.
boolean	Lexicon.contains(Feature f) Returns true if the given feature is already in the lexicon (whether it's past the Lexicon.pruneCutoff or not) and false otherwise.
void	ChildLexicon.decrementParentCounts(Feature f) The parent of feature f is being removed, so we decrement f's parent counts and remove it if it's ready.
Feature	Lexicon.getChildFeature(Feature f, int label) Used to lookup the children of conjunctive and referring features during training, this method checks Lexicon.lexiconChildren if the feature isn't present in Lexicon.lexicon and Lexicon.lexiconInv, and then stores the given feature in Lexicon.lexiconChildren if it wasn't present anywhere.
Feature	ChildLexicon.getChildFeature(Feature f, int label) This method adds the given feature to this lexicon and also recursively adds its children, if any.
protected void	ChildLexicon.incrementParentCounts(Feature f, int label) Helper method for methods like ChildLexicon.childLexiconLookup(DiscreteConjunctiveFeature,int) that actually does the work of looking up the child feature and updating its parent counts.
int	Lexicon.lookup(Feature f) Looks up a feature's index by calling lookup(f, false).
int	Lexicon.lookup(Feature f, boolean training) Looks up a feature's index by calling lookup(f, training, -1).
int	Lexicon.lookup(Feature f, boolean training, int label) Looks up the given feature in the lexicon, possibly counting it and/or expanding the lexicon to accomodate it.
int	Lexicon.lookupChild(Feature f) Used to lookup the children of conjunctive and referring features while writing the lexicon, this method checks Lexicon.lexiconChildren if the feature isn't present in Lexicon.lexicon and Lexicon.lexiconInv, and will throw an exception if it still can't be found.
int	ChildLexicon.lookupChild(Feature f) Unlike the overridden method in Lexicon, this method simply checks Lexicon.lexicon for the feature and will throw an exception if it can't be found.
Feature	ChildLexicon.remove(Feature f) Removes the mapping for the given feature from this lexicon and returns the feature object representing it that was stored here.

Uses of Feature in edu.illinois.cs.cogcomp.lbjava.util

Fields in edu.illinois.cs.cogcomp.lbjava.util declared as Feature

Modifier and Type	Field and Description
protected Feature[]	FVector.vector The elements of the vector.

Methods in edu.illinois.cs.cogcomp.lbjava.util that return Feature

Modifier and Type	Method and Description
Feature	FVector.get(int i) Retrieves the value stored at the specified index of the vector, or null if the vector isn't long enough.
Feature	FVector.get(int i, Feature d) Retrieves the value stored at the specified index of the vector or d if the vector isn't long enough.
Feature	FVector.remove(int i) Removes the element at the specified index of the vector.
Feature	FVector.set(int i, Feature v) Sets the value at the specified index to the given value.
Feature	FVector.set(int i, Feature v, Feature d) Sets the value at the specified index to the given value.
Feature[]	FVector.toArray() Returns a new array of features containing the same data as this vector.

Methods in edu.illinois.cs.cogcomp.lbjava.util with parameters of type Feature

Modifier and Type	Method and Description
void	FVector.add(Feature v) Adds the specified value on to the end of the vector, expanding its capacity as necessary.
protected void	FVector.expandFor(int index, Feature d) Makes sure the capacity and size of the vector can accomodate the given index.
Feature	FVector.get(int i, Feature d) Retrieves the value stored at the specified index of the vector or d if the vector isn't long enough.
Feature	FVector.set(int i, Feature v) Sets the value at the specified index to the given value.
Feature	FVector.set(int i, Feature v, Feature d) Sets the value at the specified index to the given value.

Constructors in edu.illinois.cs.cogcomp.lbjava.util with parameters of type Feature

Constructor and Description
FVector(Feature[] v) Constructs a new vector using the specified array as a starting point.