Uses of Interface
cc.mallet.types.Vector
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Packages that use Vector Package Description cc.mallet.types Fundamental MALLET types, including FeatureVector, Instance, Label etc. -
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Uses of Vector in cc.mallet.types
Classes in cc.mallet.types that implement Vector Modifier and Type Class Description class
AugmentableFeatureVector
class
BiNormalSeparation
Bi-Normal Separation is a feature weighting algorithm introduced in: An Extensive Empirical Study of Feature Selection Metrics for Text Classification, George Forman, Journal of Machine Learning Research, 3:1289--1305, 2003.class
DenseVector
class
ExpGain
class
FeatureCounts
class
FeatureVector
A subset of anAlphabet
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 aFeatureVector
.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
class
SparseVector
A vector that allocates memory only for non-zero values.Methods in cc.mallet.types with parameters of type Vector Modifier and Type Method Description double
FeatureVectorSequence. dotProduct(int sequencePosition, Vector weights)
static double
MatrixOps. rowDotProduct(double[] m, int nc, int ri, Vector v, double factor, int maxCi, FeatureSelection selection)
static double
MatrixOps. rowDotProduct(double[] m, int nc, int ri, Vector v, int maxCi, FeatureSelection selection)
static void
MatrixOps. rowPlusEquals(double[] m, int nc, int ri, Vector v, double factor)
static double
MatrixOps. sum(Vector v)
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