Uses of Class
cc.mallet.types.SparseVector

Packages that use SparseVector

cc.mallet.cluster	Unsupervised clustering of Instance objects within an InstanceList.
cc.mallet.fst	Transducers, including Conditional Random Fields (CRFs).
cc.mallet.grmm.learning
cc.mallet.types	Fundamental MALLET types, including FeatureVector, Instance, Label etc.
cc.mallet.util	Miscellaneous utilities including command line processing, math functions, lexing, logging.

Uses of SparseVector in cc.mallet.cluster

Methods in cc.mallet.cluster that return types with arguments of type SparseVector

java.util.ArrayList<SparseVector>

KMeans.getClusterMeans() Return the ArrayList of cluster means after a run of the algorithm.

Uses of SparseVector in cc.mallet.fst

Fields in cc.mallet.fst declared as SparseVector

SparseVector[]

CRF.Factors.weights

Methods in cc.mallet.fst that return SparseVector

SparseVector[]	CRF.getWeights()
SparseVector	CRF.getWeights(int weightIndex)
SparseVector	CRF.getWeights(java.lang.String weightName)

Methods in cc.mallet.fst with parameters of type SparseVector

void	CRF.setWeights(int weightsIndex, SparseVector transitionWeights)
void	CRF.setWeights(SparseVector[] m)
void	CRF.setWeights(java.lang.String weightName, SparseVector transitionWeights)

Uses of SparseVector in cc.mallet.grmm.learning

Fields in cc.mallet.grmm.learning declared as SparseVector

protected SparseVector[]

ACRF.Template.weights

Methods in cc.mallet.grmm.learning that return SparseVector

SparseVector[]	PwplACRFTrainer.Maxable.getConstraints(int cnum)
SparseVector[]	PseudolikelihoodACRFTrainer.Maxable.getConstraints(int cnum)
SparseVector[]	PiecewiseACRFTrainer.Maxable.getConstraints(int cnum)
SparseVector[]	ACRF.MaximizableACRF.getConstraints(int cnum)
SparseVector	ACRF.Template.getDefaultWeights()
SparseVector	ACRF.FixedFactorTemplate.getDefaultWeights()
SparseVector[]	PwplACRFTrainer.Maxable.getExpectations(int cnum)
SparseVector[]	PseudolikelihoodACRFTrainer.Maxable.getExpectations(int cnum)
SparseVector[]	PiecewiseACRFTrainer.Maxable.getExpectations(int cnum)
SparseVector[]	ACRF.MaximizableACRF.getExpectations(int cnum)
SparseVector[]	ACRF.Template.getWeights() Returns the weights for this clique template.
SparseVector[]	ACRF.FixedFactorTemplate.getWeights()

Methods in cc.mallet.grmm.learning with parameters of type SparseVector

void	ACRF.Template.setDefaultWeights(SparseVector w)
void	ACRF.Template.setWeights(SparseVector[] w)

Uses of SparseVector in cc.mallet.types

Subclasses of SparseVector in cc.mallet.types

class	AugmentableFeatureVector
class	ExpGain
class	FeatureCounts
class	FeatureVector A subset of an Alphabet in which each element of the subset has an associated value.
class	GainRatio List of features along with their thresholds sorted in descending order of the ratio of (1) information gained by splitting instances on the feature at its associated threshold value, to (2) the split information.
class	GradientGain
class	HashedSparseVector
class	IndexedSparseVector
class	InfoGain
class	KLGain
class	LabelVector
class	Multinomial A probability distribution over a set of features represented as a FeatureVector.
static class	Multinomial.Logged A Multinomial in which the values associated with each feature index fi is Math.log(probability[fi]) instead of probability[fi].
class	PartiallyRankedFeatureVector
class	RankedFeatureVector

Methods in cc.mallet.types that return SparseVector

SparseVector	AugmentableFeatureVector.toSparseVector()
SparseVector	SparseVector.vectorAdd(SparseVector v, double scale)

Methods in cc.mallet.types with parameters of type SparseVector

double	NormalizedDotProductMetric.distance(SparseVector a, int hashCodeA, SparseVector b, int hashCodeB)
double	CachedMetric.distance(SparseVector a, int hashCodeA, SparseVector b, int hashCodeB)
double	NormalizedDotProductMetric.distance(SparseVector a, SparseVector b)
double	Minkowski.distance(SparseVector a, SparseVector b) Gives the Minkowski distance between two vectors.
double	Metric.distance(SparseVector a, SparseVector b)
double	SparseVector.dotProduct(SparseVector v)
double	IndexedSparseVector.dotProduct(SparseVector v)
double	HashedSparseVector.dotProduct(SparseVector v)
double	AugmentableFeatureVector.dotProduct(SparseVector v)
double	Minkowski.euclideanDistance(SparseVector a, SparseVector b)
double	SparseVector.extendedDotProduct(SparseVector v)
void	AugmentableFeatureVector.plusEquals(SparseVector v)
void	AugmentableFeatureVector.plusEquals(SparseVector v, double factor)
void	SparseVector.plusEqualsSparse(SparseVector v) For each index i that is present in this vector, set this[i] += v[i].
void	IndexedSparseVector.plusEqualsSparse(SparseVector v)
void	HashedSparseVector.plusEqualsSparse(SparseVector v)
void	SparseVector.plusEqualsSparse(SparseVector v, double factor) For each index i that is present in this vector, set this[i] += factor * v[i].
void	IndexedSparseVector.plusEqualsSparse(SparseVector v, double factor)
void	HashedSparseVector.plusEqualsSparse(SparseVector v, double factor)
void	SparseVector.timesEqualsSparse(SparseVector v) For each index i that is present in this vector, set this[i] *= v[i].
void	SparseVector.timesEqualsSparse(SparseVector v, double factor) For each index i that is present in this vector, set this[i] *= factor * v[i].
void	SparseVector.timesEqualsSparseZero(SparseVector v, double factor) For each index i that is present in this vector, set this[i] *= factor * v[i].
SparseVector	SparseVector.vectorAdd(SparseVector v, double scale)

Constructors in cc.mallet.types with parameters of type SparseVector

PartiallyRankedFeatureVector(Alphabet dict, SparseVector v)

RankedFeatureVector(Alphabet dict, SparseVector v)

Uses of SparseVector in cc.mallet.util

Methods in cc.mallet.util that return SparseVector

static SparseVector	VectorStats.mean(InstanceList instances) Returns a SparseVector whose entries (taken from the union of those in the instances) are the expected values of those in the InstanceList.
static SparseVector	VectorStats.mean(InstanceList instances, int numIndices) Returns a SparseVector whose entries (dense with the given number of indices) are the expected values of those in the InstanceList.
static SparseVector	VectorStats.mean(InstanceList instances, int[] indices) Returns a SparseVector whose entries (the given indices) are the expected values of those in the InstanceList.
static SparseVector	VectorStats.stddev(InstanceList instances) Square root of unbiased variance.
static SparseVector	VectorStats.stddev(InstanceList instances, boolean unbiased) Square root of variance.
static SparseVector	VectorStats.stddev(InstanceList instances, SparseVector mean) Square root of unbiased variance of instances having the given mean
static SparseVector	VectorStats.stddev(InstanceList instances, SparseVector mean, boolean unbiased) Square root of variance.
static SparseVector	VectorStats.variance(InstanceList instances) Returns unbiased variance
static SparseVector	VectorStats.variance(InstanceList instances, boolean unbiased) Returns a SparseVector whose entries (taken from the union of those in the instances) are the variance of those in the InstanceList.
static SparseVector	VectorStats.variance(InstanceList instances, SparseVector mean) Returns unbiased variance of instances having the given mean.
static SparseVector	VectorStats.variance(InstanceList instances, SparseVector mean, boolean unbiased) Returns a SparseVector whose entries (taken from the mean argument) are the variance of those in the InstanceList.

Methods in cc.mallet.util with parameters of type SparseVector

static SparseVector	VectorStats.stddev(InstanceList instances, SparseVector mean) Square root of unbiased variance of instances having the given mean
static SparseVector	VectorStats.stddev(InstanceList instances, SparseVector mean, boolean unbiased) Square root of variance.
static SparseVector	VectorStats.variance(InstanceList instances, SparseVector mean) Returns unbiased variance of instances having the given mean.
static SparseVector	VectorStats.variance(InstanceList instances, SparseVector mean, boolean unbiased) Returns a SparseVector whose entries (taken from the mean argument) are the variance of those in the InstanceList.