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

Packages that use FeatureVector

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

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

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

Modifier and Type	Field and Description
protected FeatureVector	FeatureVector.realCache Caches the result of the makeReal() method.

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

Modifier and Type	Method and Description
abstract FeatureVector	Classifier.classify(Object o) This method makes one or more decisions about a single object, returning those decisions as Features in a vector.
FeatureVector	ValueComparer.classify(Object o) Returns a Boolean feature (with value "true" or "false") representing the equality of the output of ValueComparer.labeler applied to the argument object and ValueComparer.value.
FeatureVector	MultiValueComparer.classify(Object o) Returns a Boolean feature (with value "true" or "false") indicating whether the output of ValueComparer.labeler applied to the argument object contained the feature value referenced by ValueComparer.value.
FeatureVector	LabelVectorReturner.classify(Object o) This method makes one or more decisions about a single object, returning those decisions as Features in a vector.
FeatureVector	FeatureVectorReturner.classify(Object o) This method makes one or more decisions about a single object, returning those decisions as Features in a vector.
FeatureVector[]	Classifier.classify(Object[] o) Use this method to make a batch of classification decisions about several objects.
FeatureVector	FeatureVector.makeReal() Converts all of the features in the features list to RealFeatures with appropriate strengths.

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

Modifier and Type	Method and Description
void	FeatureVector.addFeatures(FeatureVector v) Adds all the features in another vector to this vector.
void	FeatureVector.addLabels(FeatureVector v) Adds all the features in another vector (but not the labels in that vector) to the labels of this vector.
double	FeatureVector.dot(FeatureVector vector) Take the dot product of two feature vectors.
boolean	FeatureVector.valueEquals(FeatureVector vector) Two FeatureVectors have equal value if they contain the same number of Features and if the values of those Features are pair-wise equivalent according to the Feature.valueEquals(String) method.

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

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

Modifier and Type	Method and Description
FeatureVector	ParameterizedConstraint.classify(Object o) This method makes one or more decisions about a single object, returning those decisions as Features in a vector.

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

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

Modifier and Type	Method and Description
FeatureVector	Learner.classify(FeatureVector vector) This method makes one or more decisions about a single feature vector, returning those decisions as Features in a vector.
FeatureVector[]	Learner.classify(FeatureVector[] vectors) Use this method to make a batch of classification decisions about several objects.
FeatureVector	SparseMIRA.classify(int[] exampleFeatures, double[] exampleValues) This implementation uses a winner-take-all comparison of the individual weight vectors' dot products.
FeatureVector	MuxLearner.classify(int[] exampleFeatures, double[] exampleValues) This method performs the multiplexing and returns the output of the selected Learner.
FeatureVector	SupportVectorMachine.classify(int[] exampleFeatures, double[] exampleValues) Evaluates the given example using liblinear's prediction method.
FeatureVector	SparseNetworkLearner.classify(int[] exampleFeatures, double[] exampleValues) This implementation uses a winner-take-all comparison of the outputs from the individual linear threshold units' score methods.
FeatureVector	AdaGrad.classify(int[] exampleFeatures, double[] exampleValues) Simply computes the dot product of the weight vector and the feature vector extracted from the example object.
FeatureVector	NaiveBayes.classify(int[] exampleFeatures, double[] exampleValues) Prediction value counts and feature counts given a particular prediction value are used to select the most likely prediction value.
FeatureVector	WekaWrapper.classify(int[] exampleFeatures, double[] exampleValues) This method makes one or more decisions about a single object, returning those decisions as Features in a vector.
FeatureVector	StochasticGradientDescent.classify(int[] exampleFeatures, double[] exampleValues) Simply computes the dot product of the weight vector and the feature vector extracted from the example object.
FeatureVector	LinearThresholdUnit.classify(int[] exampleFeatures, double[] exampleValues) The default evaluation method simply computes the score for the example and returns a DiscretePrimitiveStringFeature set to either the second value from the label classifier's array of allowable values if the score is greater than or equal to LinearThresholdUnit.threshold or the first otherwise.
abstract FeatureVector	Learner.classify(int[] exampleFeatures, double[] exampleValues) This method makes one or more decisions about a single object, returning those decisions as Features in a vector.
FeatureVector	AdaBoost.classify(int[] exampleFeatures, double[] exampleValues) This method uses the trained parameters to make a binary decision about an example object.
FeatureVector	MultiLabelLearner.classify(int[] exampleFeatures, double[] exampleValues) Returns a separate feature for each LinearThresholdUnit whose score on the example object exceeds the threshold.
FeatureVector	Learner.classify(Object example) This method makes one or more decisions about a single object, returning those decisions as Features in a vector.
FeatureVector[]	Learner.classify(Object[][] e) Use this method to make a batch of classification decisions about several examples.

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

Modifier and Type	Method and Description
FeatureVector	Learner.classify(FeatureVector vector) This method makes one or more decisions about a single feature vector, returning those decisions as Features in a vector.
FeatureVector[]	Learner.classify(FeatureVector[] vectors) Use this method to make a batch of classification decisions about several objects.
String	Learner.discreteValue(FeatureVector vector) Returns the value of the discrete prediction that this learner would make, given a feature vector.
Feature	Learner.featureValue(FeatureVector vector) Returns the classification of the given feature vector as a single feature instead of a FeatureVector.
void	Learner.learn(FeatureVector vector) Trains the learning algorithm given a feature vector as an example.
void	Learner.learn(FeatureVector[] examples) Trains the learning algorithm given many feature vectors as examples.
double	Learner.realValue(FeatureVector vector) Returns the value of the real prediction that this learner would make, given a feature vector.
ScoreSet	Learner.scores(FeatureVector vector) Produces a set of scores indicating the degree to which each possible discrete classification value is associated with the given feature vector.