Uses of Class
edu.illinois.cs.cogcomp.lbjava.learn.SparseWeightVector

Packages that use SparseWeightVector

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

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

Subclasses of SparseWeightVector in edu.illinois.cs.cogcomp.lbjava.learn

Modifier and Type	Class and Description
class	BiasedRandomWeightVector Same as the RandomWeightVector class that it extends, except that this vector also contains a bias term (also initialized randomly) which is added to every dot product and affected by every vector addition operation.
class	BiasedWeightVector Same as the SparseWeightVector class that it extends, except that this vector also contains a bias term which is added to every dot product and affected by every vector addition operation.
protected class	NaiveBayes.NaiveBayesVector Keeps track of all the counts associated with a given label.
class	RandomWeightVector This weight vector operates similarly to its parent in the class hierarchy, but it halucinates (and sets) random values for weights corresponding to features it has never been asked about before.
static class	SparseAveragedPerceptron.AveragedWeightVector This implementation of a sparse weight vector associates two doubles with each Feature.

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

Modifier and Type	Field and Description
static SparseWeightVector	StochasticGradientDescent.defaultWeightVector Default for StochasticGradientDescent.weightVector.
static SparseWeightVector	LinearThresholdUnit.defaultWeightVector Default for LinearThresholdUnit.weightVector.
protected SparseWeightVector	SparseConfidenceWeighted.variances The inverses of the current variances of the parameters.
protected SparseWeightVector	SparseConfidenceWeighted.Parameters.variances The current variances of the parameters; default LinearThresholdUnit.defaultWeightVector.
protected SparseWeightVector	StochasticGradientDescent.weightVector The hypothesis vector; default StochasticGradientDescent.defaultWeightVector.
SparseWeightVector	StochasticGradientDescent.Parameters.weightVector The hypothesis vector; default StochasticGradientDescent.defaultWeightVector.
protected SparseWeightVector	LinearThresholdUnit.weightVector The LTU's weight vector; default is an empty vector.
SparseWeightVector	LinearThresholdUnit.Parameters.weightVector The LTU's weight vector; default is an empty vector.

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

Modifier and Type	Method and Description
SparseWeightVector	RandomWeightVector.emptyClone() Returns a new, empty weight vector with the same parameter settings as this one.
SparseWeightVector	BiasedWeightVector.emptyClone() Returns a new, empty weight vector with the same parameter settings as this one.
SparseWeightVector	NaiveBayes.NaiveBayesVector.emptyClone() Returns a new, empty weight vector with the same parameter settings as this one.
SparseWeightVector	SparseWeightVector.emptyClone() Returns a new, empty weight vector with the same parameter settings as this one.
SparseWeightVector	SparseAveragedPerceptron.AveragedWeightVector.emptyClone() Returns a new, empty weight vector with the same parameter settings as this one.
SparseWeightVector	BiasedRandomWeightVector.emptyClone() Returns a new, empty weight vector with the same parameter settings as this one.
SparseWeightVector	LinearThresholdUnit.getWeightVector()
static SparseWeightVector	SparseWeightVector.readWeightVector(edu.illinois.cs.cogcomp.core.datastructures.vectors.ExceptionlessInputStream in) Reads the binary representation of a weight vector of any type from the given stream.

Constructors in edu.illinois.cs.cogcomp.lbjava.learn with parameters of type SparseWeightVector

Constructor and Description
BinaryMIRA(double r, double B, SparseWeightVector v) Sets the learning rate, beta and the weight vector to the specified values.
BinaryMIRA(String n, double r, double B, SparseWeightVector v) Sets the name of the classifier, the learning rate, beta and the weight vector to the specified values.
LinearThresholdUnit(String n, double r, double t, double pt, double nt, SparseWeightVector v) Initializing constructor.
PassiveAggressive(double t, double pt, double nt, SparseWeightVector v) Use this constructor to specify an alternative subclass of SparseWeightVector, while the name of the classifier gets the empty string.
PassiveAggressive(String n, double t, double pt, double nt, SparseWeightVector v) Use this constructor to specify an alternative subclass of SparseWeightVector.
SparseConfidenceWeighted(double c, double v, SparseWeightVector vm) Sets the SparseConfidenceWeighted.confidence, SparseConfidenceWeighted.initialVariance, and LinearThresholdUnit.weightVector parameters.
SparseConfidenceWeighted(double c, double v, SparseWeightVector vm, SparseWeightVector vv) Sets the SparseConfidenceWeighted.confidence, SparseConfidenceWeighted.initialVariance, LinearThresholdUnit.weightVector, and SparseConfidenceWeighted.variances parameters.
SparseConfidenceWeighted(String n, double c, double v, SparseWeightVector vm) Sets the SparseConfidenceWeighted.confidence, SparseConfidenceWeighted.initialVariance, and LinearThresholdUnit.weightVector parameters.
SparseConfidenceWeighted(String n, double c, double v, SparseWeightVector vm, SparseWeightVector vv) Sets the SparseConfidenceWeighted.confidence, SparseConfidenceWeighted.initialVariance, LinearThresholdUnit.weightVector, and SparseConfidenceWeighted.variances parameters.
SparsePerceptron(double r, double t, double pt, double nt, SparseWeightVector v) Use this constructor to specify an alternative subclass of SparseWeightVector, while the name of the classifier gets the empty string.
SparsePerceptron(String n, double r, double t, double pt, double nt, SparseWeightVector v) Use this constructor to specify an alternative subclass of SparseWeightVector.
SparseWinnow(double a, double b, double t, double pt, double nt, SparseWeightVector v) Use this constructor to specify an alternative subclass of SparseWeightVector, while the name of the classifier gets the empty string.
SparseWinnow(String n, double a, double b, double t, double pt, double nt, SparseWeightVector v) Use this constructor to specify an alternative subclass of SparseWeightVector.