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## 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[]` | `ACRF.MaximizableACRF.getConstraints(int cnum)`
`SparseVector` | `ACRF.Template.getDefaultWeights()`
`SparseVector` | `ACRF.FixedFactorTemplate.getDefaultWeights()`
`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` | `CachedMetric.distance(SparseVector a, int hashCodeA, SparseVector b, int hashCodeB)`
`double` | `NormalizedDotProductMetric.distance(SparseVector a, int hashCodeA, SparseVector b, int hashCodeB)`
`double` | `Minkowski.distance(SparseVector a, SparseVector b)` Gives the Minkowski distance between two vectors.
`double` | `Metric.distance(SparseVector a, SparseVector b)`
`double` | `NormalizedDotProductMetric.distance(SparseVector a, SparseVector b)`
`double` | `AugmentableFeatureVector.dotProduct(SparseVector v)`
`double` | `SparseVector.dotProduct(SparseVector v)`
`double` | `HashedSparseVector.dotProduct(SparseVector v)`
`double` | `IndexedSparseVector.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` | `HashedSparseVector.plusEqualsSparse(SparseVector v)`
`void` | `IndexedSparseVector.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` | `HashedSparseVector.plusEqualsSparse(SparseVector v, double factor)`
`void` | `IndexedSparseVector.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`.

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