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Packages that use Randoms | |
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cc.mallet.cluster.iterator | |
cc.mallet.cluster.neighbor_evaluator | |
cc.mallet.cluster.util | |
cc.mallet.grmm.inference | |
cc.mallet.grmm.types | |
cc.mallet.pipe.iterator | Classes that generate instances from different kinds of input or data structures. |
cc.mallet.topics | |
cc.mallet.types | Fundamental MALLET types, including FeatureVector, Instance, Label etc. |
Uses of Randoms in cc.mallet.cluster.iterator |
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Fields in cc.mallet.cluster.iterator declared as Randoms | |
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protected Randoms |
PairSampleIterator.random
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Methods in cc.mallet.cluster.iterator with parameters of type Randoms | |
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protected int[] |
ClusterSampleIterator.sampleFromArray(int[] a,
Randoms random,
int minSize)
Samples a subset of elements from this array. |
protected int[][] |
ClusterSampleIterator.sampleSplitFromArray(int[] a,
Randoms random,
int minSize)
Samples a two disjoint subset of elements from this array. |
Constructors in cc.mallet.cluster.iterator with parameters of type Randoms | |
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ClusterSampleIterator(Clustering clustering,
Randoms random,
double positiveProportion,
int numberSamples)
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NodeClusterSampleIterator(Clustering clustering,
Randoms random,
double positiveProportion,
int numberSamples)
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PairSampleIterator(Clustering clustering,
Randoms random,
double positiveProportion,
int numberSamples)
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Uses of Randoms in cc.mallet.cluster.neighbor_evaluator |
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Constructors in cc.mallet.cluster.neighbor_evaluator with parameters of type Randoms | |
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RandomEvaluator(Randoms random)
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Uses of Randoms in cc.mallet.cluster.util |
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Methods in cc.mallet.cluster.util with parameters of type Randoms | |
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static Clustering |
ClusterUtils.createRandomClustering(InstanceList instances,
Randoms random)
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Uses of Randoms in cc.mallet.grmm.inference |
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Methods in cc.mallet.grmm.inference with parameters of type Randoms | |
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static FactorGraph |
RandomGraphs.createRandomChain(Randoms r,
int length)
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void |
GibbsSampler.setRandom(Randoms r)
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void |
Sampler.setRandom(Randoms r)
Sets the random seed used by this sampler. |
void |
ExactSampler.setRandom(Randoms r)
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Constructors in cc.mallet.grmm.inference with parameters of type Randoms | |
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ExactSampler(Randoms r)
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GibbsSampler(Randoms r,
int burnin)
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Uses of Randoms in cc.mallet.grmm.types |
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Methods in cc.mallet.grmm.types with parameters of type Randoms | |
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Assignment |
UniformFactor.sample(Randoms r)
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Assignment |
UniNormalFactor.sample(Randoms r)
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Assignment |
SkeletonFactor.sample(Randoms r)
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Assignment |
NormalFactor.sample(Randoms r)
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Assignment |
ConstantFactor.sample(Randoms r)
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Assignment |
BoltzmannUnaryFactor.sample(Randoms r)
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Assignment |
BoltzmannPairFactor.sample(Randoms r)
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Assignment |
BinaryUnaryFactor.sample(Randoms r)
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Assignment |
BetaFactor.sample(Randoms r)
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Assignment |
PottsTableFactor.sample(Randoms r)
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Assignment |
CPT.sample(Randoms r)
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Assignment |
AbstractTableFactor.sample(Randoms r)
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Assignment |
AbstractFactor.sample(Randoms r)
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Assignment |
Assignment.sample(Randoms r)
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Assignment |
Factor.sample(Randoms r)
Return an assignment sampled from this factor, interpreting it as an unnormalized probability distribution. |
Assignment |
FactorGraph.sample(Randoms r)
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Assignment |
FactorGraph.sampleContinuousVars(Randoms r)
Samples the continuous variables in this factor graph. |
int |
CPT.sampleLocation(Randoms r)
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int |
DiscreteFactor.sampleLocation(Randoms r)
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int |
AbstractTableFactor.sampleLocation(Randoms r)
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Uses of Randoms in cc.mallet.pipe.iterator |
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Constructors in cc.mallet.pipe.iterator with parameters of type Randoms | |
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RandomFeatureVectorIterator(Randoms r,
Alphabet vocab,
java.lang.String[] classnames)
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RandomFeatureVectorIterator(Randoms r,
Dirichlet classCentroidDistribution,
double classCentroidAvergeAlphaMean,
double classCentroidAvergeAlphaVariance,
double featureVectorSizePoissonLambda,
double classInstanceCountPoissonLamba,
java.lang.String[] classNames)
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RandomFeatureVectorIterator(Randoms r,
int vocabSize,
int numClasses)
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RandomTokenSequenceIterator(Randoms r,
Alphabet vocab,
java.lang.String[] classnames)
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RandomTokenSequenceIterator(Randoms r,
Dirichlet classCentroidDistribution,
double classCentroidAvergeAlphaMean,
double classCentroidAvergeAlphaVariance,
double featureVectorSizePoissonLambda,
double classInstanceCountPoissonLamba,
java.lang.String[] classNames)
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RandomTokenSequenceIterator(Randoms r,
int vocabSize,
int numClasses)
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Uses of Randoms in cc.mallet.topics |
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Fields in cc.mallet.topics declared as Randoms | |
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protected Randoms |
LDAHyper.random
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protected Randoms |
TopicInferencer.random
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protected Randoms |
WorkerRunnable.random
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Methods in cc.mallet.topics with parameters of type Randoms | |
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void |
LDA.addDocuments(InstanceList additionalDocuments,
int numIterations,
int showTopicsInterval,
int outputModelInterval,
java.lang.String outputModelFilename,
Randoms r)
Deprecated. |
void |
PAM4L.estimate(InstanceList documents,
int numIterations,
int optimizeInterval,
int showTopicsInterval,
int outputModelInterval,
java.lang.String outputModelFilename,
Randoms r)
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void |
TopicalNGrams.estimate(InstanceList documents,
int numIterations,
int showTopicsInterval,
int outputModelInterval,
java.lang.String outputModelFilename,
Randoms r)
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void |
LDA.estimate(InstanceList documents,
int numIterations,
int showTopicsInterval,
int outputModelInterval,
java.lang.String outputModelFilename,
Randoms r)
Deprecated. |
void |
LDA.estimate(int docIndexStart,
int docIndexLength,
int numIterations,
int showTopicsInterval,
int outputModelInterval,
java.lang.String outputModelFilename,
Randoms r)
Deprecated. |
void |
HierarchicalLDA.initialize(InstanceList instances,
InstanceList testing,
int numLevels,
Randoms random)
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void |
LDA.sampleTopicsForAllDocs(Randoms r)
Deprecated. |
void |
LDA.sampleTopicsForDocs(int start,
int length,
Randoms r)
Deprecated. |
Constructors in cc.mallet.topics with parameters of type Randoms | |
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LDAHyper(int numberOfTopics,
double alphaSum,
double beta,
Randoms random)
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LDAHyper(LabelAlphabet topicAlphabet,
double alphaSum,
double beta,
Randoms random)
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LDAStream(int numberOfTopics,
double alphaSum,
double beta,
Randoms random)
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LDAStream(LabelAlphabet topicAlphabet,
double alphaSum,
double beta,
Randoms random)
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WorkerRunnable(int numTopics,
double[] alpha,
double alphaSum,
double beta,
Randoms random,
java.util.ArrayList<TopicAssignment> data,
int[][] typeTopicCounts,
int[] tokensPerTopic,
int startDoc,
int numDocs)
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Uses of Randoms in cc.mallet.types |
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Methods in cc.mallet.types with parameters of type Randoms | |
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void |
InstanceList.hideSomeLabels(double proportionToHide,
Randoms r)
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Dirichlet |
Dirichlet.randomDirichlet(Randoms r,
double averageAlpha)
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FeatureSequence |
Multinomial.randomFeatureSequence(Randoms r,
int length)
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FeatureSequence |
Dirichlet.randomFeatureSequence(Randoms r,
int length)
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FeatureVector |
Multinomial.randomFeatureVector(Randoms r,
int size)
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FeatureVector |
Dirichlet.randomFeatureVector(Randoms r,
int size)
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int |
Multinomial.randomIndex(Randoms r)
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Multinomial |
Dirichlet.randomMultinomial(Randoms r)
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java.lang.Object |
Multinomial.randomObject(Randoms r)
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protected double[] |
Dirichlet.randomRawMultinomial(Randoms r)
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TokenSequence |
Dirichlet.randomTokenSequence(Randoms r,
int length)
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double[] |
Dirichlet.randomVector(Randoms r)
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Constructors in cc.mallet.types with parameters of type Randoms | |
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InstanceList(Randoms r,
Alphabet vocab,
java.lang.String[] classNames,
int meanInstancesPerLabel)
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InstanceList(Randoms r,
Dirichlet classCentroidDistribution,
double classCentroidAverageAlphaMean,
double classCentroidAverageAlphaVariance,
double featureVectorSizePoissonLambda,
double classInstanceCountPoissonLambda,
java.lang.String[] classNames)
Creates a list consisting of randomly-generated FeatureVector s. |
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InstanceList(Randoms r,
int vocabSize,
int numClasses)
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