Uses of Class
LBJ2.classify.FeatureVector

Packages that use FeatureVector

LBJ2.classify	Contains classes representing classifiers and features, as well as utility classes related to classifiers and features that may come in handy.
LBJ2.infer	Inference algorithms are implemented here (derived from Inference), but most of the classes in this package are used internally by LBJ at runtime to represent constraints and to translate between constraint representations.
LBJ2.learn	Learning algorithms, normalizers (used in inference; see Normalizer), testing metrics (used in cross validation; see TestingMetric), and other utility classes can be found in this package.
LBJ2.nlp	Parsers, data structures, pre-processing algorithms, and common feature extracting classifiers (implemented with LBJ) useful for natural language processing are implemented in this package.

Uses of FeatureVector in LBJ2.classify

Methods in LBJ2.classify that return FeatureVector

FeatureVector	LabelVectorReturner.classify(java.lang.Object o) This method makes one or more decisions about a single object, returning those decisions as Features in a vector.
FeatureVector	MultiValueComparer.classify(java.lang.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	FeatureVectorReturner.classify(java.lang.Object o) This method makes one or more decisions about a single object, returning those decisions as Features in a vector.
FeatureVector	ValueComparer.classify(java.lang.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.
abstract FeatureVector	Classifier.classify(java.lang.Object o) This method makes one or more decisions about a single object, returning those decisions as Features in a vector.
FeatureVector[]	Classifier.classify(java.lang.Object[] o) Use this method to make a batch of classification decisions about several objects.

Methods in LBJ2.classify with parameters of type FeatureVector

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 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 Feature's valueEquals(String) method.

Uses of FeatureVector in LBJ2.infer

Methods in LBJ2.infer that return FeatureVector

FeatureVector

ParameterizedConstraint.classify(java.lang.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 LBJ2.learn

Fields in LBJ2.learn declared as FeatureVector

protected FeatureVector

SparseWeightVector.WeightIterator.vector The feature vector to iterate through.

Methods in LBJ2.learn that return FeatureVector

FeatureVector	StochasticGradientDescent.classify(java.lang.Object example) Simply computes the dot product of the weight vector and the feature vector extracted from the example object.
FeatureVector	WekaWrapper.classify(java.lang.Object example) This method makes one or more decisions about a single object, returning those decisions as Features in a vector.
FeatureVector	MuxLearner.classify(java.lang.Object example) This method performs the multiplexing and returns the output of the selected Learner.
FeatureVector	SparseNetworkLearner.classify(java.lang.Object example) This implementation uses a winner-take-all comparison of the outputs from the individual linear threshold units' score methods.
FeatureVector	AdaBoost.classify(java.lang.Object example) This method uses the trained parameters to make a binary decision about an example object.
FeatureVector	NaiveBayes.classify(java.lang.Object example) Prediction value counts and feature counts given a particular prediction value are used to select the most likely prediction value.
FeatureVector	MultiLabelLearner.classify(java.lang.Object example) Returns a separate feature for each LinearThresholdUnit whose score on the example object exceeds the threshold.
FeatureVector	LinearThresholdUnit.classify(java.lang.Object example) The default evaluation method simply computes the score for the example and returns a DiscreteFeature 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.

Methods in LBJ2.learn with parameters of type FeatureVector

double	SparseWeightVector.dot(FeatureVector example) Takes the dot product of this SparseWeightVector with the argument vector, using the hard coded default weight.
double	NaiveBayes.NaiveBayesVector.dot(FeatureVector example) Takes the dot product of this vector with the given vector, using the hard coded smoothing weight.
double	SparseWeightVector.dot(FeatureVector example, double defaultW) Takes the dot product of this SparseWeightVector with the argument vector, using the specified default weight when one is not yet present in this vector.
double	NaiveBayes.NaiveBayesVector.dot(FeatureVector example, double defaultW) Takes the dot product of this vector with the given vector, using the specified default weight when encountering a feature that is not yet present in this vector.
private weka.core.Instance	WekaWrapper.makeInstance(FeatureVector example, FeatureVector labels) Creates a WEKA Instance object out of a FeatureVector.
void	SparseWeightVector.scaledAdd(FeatureVector example) Self-modifying vector addition.
void	SparseWeightVector.scaledAdd(FeatureVector example, double factor) Self-modifying vector addition where the argument vector is first scaled by the given factor.
void	SparseWeightVector.scaledAdd(FeatureVector example, double factor, double defaultW) Self-modifying vector addition where the argument vector is first scaled by the given factor.
void	SparseAveragedPerceptron.AveragedWeightVector.scaledAdd(FeatureVector example, double factor, double defaultW) Performs pairwise addition of the feature values in the given vector scaled by the given factor, modifying this weight vector, using the specified default weight when a feature from the given vector is not yet present in this vector.
void	NaiveBayes.NaiveBayesVector.scaledAdd(FeatureVector example, double factor, double defaultW) This method is similar to the implementation in SparseWeightVector except that the factor and defaultW arguments are ignored and NaiveBayes.NaiveBayesVector.NaiveBayesIterator.incrementCount(double) is called instead of SparseWeightVector.WeightIterator.setWeight(double).
double	SparseAveragedPerceptron.AveragedWeightVector.simpleDot(FeatureVector example) Takes the dot product of the regular, non-averaged, Perceptron weight vector with the given vector, using the hard coded default weight.
double	SparseAveragedPerceptron.AveragedWeightVector.simpleDot(FeatureVector example, double defaultW) Takes the dot product of the regular, non-averaged, Perceptron weight vector with the given vector, using the specified default weight when a feature is not yet present in this vector.
SparseWeightVector.WeightIterator	SparseWeightVector.weightIterator(FeatureVector example) Produces an iterator that accesses the data in this vector associated with the features in the argument vector.
SparseWeightVector.WeightIterator	SparseAveragedPerceptron.AveragedWeightVector.weightIterator(FeatureVector example) Produces an iterator that accesses the data in this vector associated with the features in the given vector.
SparseWeightVector.WeightIterator	NaiveBayes.NaiveBayesVector.weightIterator(FeatureVector example) Produces an iterator that accesses the data in this vector associated with the features in the given vector.

Constructors in LBJ2.learn with parameters of type FeatureVector

NaiveBayes.NaiveBayesVector.NaiveBayesIterator(FeatureVector example)
This constructor selects a slice of weights from the NaiveBayes.NaiveBayesVector representing all those weights corresponding to features in the given vector.

SparseAveragedPerceptron.AveragedWeightVector.AveragedWeightIterator(FeatureVector example)
This constructor selects a slice of weights from the SparseAveragedPerceptron.AveragedWeightVector representing all those weights corresponding to features in the given vector.

SparseWeightVector.WeightIterator(FeatureVector example)
This constructor selects a slice of weights from the SparseWeightVector representing all those weights corresponding to features in the given vector.

Uses of FeatureVector in LBJ2.nlp

Methods in LBJ2.nlp that return FeatureVector

FeatureVector	Affixes.classify(java.lang.Object __example)
FeatureVector	Capitalization.classify(java.lang.Object __example)
FeatureVector	Forms.classify(java.lang.Object __example)
FeatureVector	WordTypeInformation.classify(java.lang.Object __example)
FeatureVector[]	Affixes.classify(java.lang.Object[] examples)
FeatureVector[]	Capitalization.classify(java.lang.Object[] examples)
FeatureVector[]	Forms.classify(java.lang.Object[] examples)
FeatureVector[]	WordTypeInformation.classify(java.lang.Object[] examples)