Uses of Class cc.mallet.util.Randoms (Mallet 2 API)

Packages that use Randoms
Package	Description
cc.mallet.cluster.iterator
cc.mallet.cluster.neighbor_evaluator
cc.mallet.cluster.util
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.
cc.mallet.util	Miscellaneous utilities including command line processing, math functions, lexing, logging.

Uses of Randoms in cc.mallet.cluster.iterator

Fields in cc.mallet.cluster.iterator declared as Randoms
Modifier and Type	Field	Description
`protected Randoms`	PairSampleIterator.`random`

Methods in cc.mallet.cluster.iterator with parameters of type Randoms
Modifier and Type	Method	Description
`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
Constructor	Description
`ClusterSampleIterator(Clustering clustering, Randoms random, double positiveProportion, int numberSamples)`
`NodeClusterSampleIterator(Clustering clustering, Randoms random, double positiveProportion, int numberSamples)`
`PairSampleIterator(Clustering clustering, Randoms random, double positiveProportion, int numberSamples)`

Uses of Randoms in cc.mallet.cluster.neighbor_evaluator

Constructors in cc.mallet.cluster.neighbor_evaluator with parameters of type Randoms
Constructor Description

RandomEvaluator(Randoms random)

Uses of Randoms in cc.mallet.cluster.util

Methods in cc.mallet.cluster.util with parameters of type Randoms
Modifier and Type	Method	Description
`static Clustering`	ClusterUtils.`createRandomClustering(InstanceList instances, Randoms random)`

Uses of Randoms in cc.mallet.pipe.iterator

Constructors in cc.mallet.pipe.iterator with parameters of type Randoms
Constructor	Description
`RandomFeatureVectorIterator(Randoms r, int vocabSize, int numClasses)`
`RandomFeatureVectorIterator(Randoms r, Alphabet vocab, java.lang.String[] classnames)`
`RandomFeatureVectorIterator(Randoms r, Dirichlet classCentroidDistribution, double classCentroidAvergeAlphaMean, double classCentroidAvergeAlphaVariance, double featureVectorSizePoissonLambda, double classInstanceCountPoissonLamba, java.lang.String[] classNames)`
`RandomTokenSequenceIterator(Randoms r, int vocabSize, int numClasses)`
`RandomTokenSequenceIterator(Randoms r, Alphabet vocab, java.lang.String[] classnames)`
`RandomTokenSequenceIterator(Randoms r, Dirichlet classCentroidDistribution, double classCentroidAvergeAlphaMean, double classCentroidAvergeAlphaVariance, double featureVectorSizePoissonLambda, double classInstanceCountPoissonLamba, java.lang.String[] classNames)`

Uses of Randoms in cc.mallet.topics

Fields in cc.mallet.topics declared as Randoms
Modifier and Type	Field	Description
`protected Randoms`	LabeledLDA.`random`
`protected Randoms`	LDAHyper.`random`	Deprecated.
`protected Randoms`	MarginalProbEstimator.`random`
`protected Randoms`	NPTopicModel.`random`
`protected Randoms`	PolylingualTopicModel.`random`
`protected Randoms`	SimpleLDA.`random`
`protected Randoms`	TopicInferencer.`random`
`protected Randoms`	WeightedTopicModel.`random`
`protected Randoms`	WorkerCallable.`random`
`protected Randoms`	WorkerRunnable.`random`

Methods in cc.mallet.topics with parameters of type Randoms
Modifier and Type	Method	Description
`void`	LDA.`addDocuments(InstanceList additionalDocuments, int numIterations, int showTopicsInterval, int outputModelInterval, java.lang.String outputModelFilename, Randoms r)`	Deprecated.
`void`	HierarchicalPAM.`estimate(InstanceList documents, InstanceList testing, int numIterations, int showTopicsInterval, int outputModelInterval, int optimizeInterval, java.lang.String outputModelFilename, Randoms r)`
`void`	LDA.`estimate(int docIndexStart, int docIndexLength, int numIterations, int showTopicsInterval, int outputModelInterval, java.lang.String outputModelFilename, Randoms r)`	Deprecated.
`void`	LDA.`estimate(InstanceList documents, 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)`
`void`	TopicalNGrams.`estimate(InstanceList documents, int numIterations, int showTopicsInterval, int outputModelInterval, java.lang.String outputModelFilename, Randoms r)`
`void`	HierarchicalLDA.`initialize(InstanceList instances, InstanceList testing, int numLevels, Randoms random)`
`void`	LDA.`sampleTopicsForAllDocs(Randoms r)`	Deprecated.
`void`	LDA.`sampleTopicsForDocs(int start, int length, Randoms r)`	Deprecated.
`void`	MarginalProbEstimator.`setRandom(Randoms r)`

Constructors in cc.mallet.topics with parameters of type Randoms
Constructor	Description
`DMRCallable(int numTopics, DMRTopicModel model, double beta, Randoms random, java.util.ArrayList<TopicAssignment> data, int[][] typeTopicCounts, int[] tokensPerTopic, int startDoc, int numDocs)`
`LDAHyper(int numberOfTopics, double alphaSum, double beta, Randoms random)`	Deprecated.
`LDAHyper(LabelAlphabet topicAlphabet, double alphaSum, double beta, Randoms random)`	Deprecated.
`LDAStream(int numberOfTopics, double alphaSum, double beta, Randoms random)`
`LDAStream(LabelAlphabet topicAlphabet, double alphaSum, double beta, Randoms random)`
`NonNegativeMatrixFactorization(InstanceList instances, int numFactors, boolean idfWeighting, Randoms random)`
`PolylingualTopicModel(int numberOfTopics, double alphaSum, Randoms random)`
`PolylingualTopicModel(LabelAlphabet topicAlphabet, double alphaSum, Randoms random)`
`SimpleLDA(int numberOfTopics, double alphaSum, double beta, Randoms random)`
`SimpleLDA(LabelAlphabet topicAlphabet, double alphaSum, double beta, Randoms random)`
`WeightedTopicModel(int numberOfTopics, double alphaSum, double beta, Randoms random)`
`WorkerCallable(int numTopics, double[] alpha, double alphaSum, double beta, Randoms random, java.util.ArrayList<TopicAssignment> data, int[][] typeTopicCounts, int[] tokensPerTopic, int startDoc, int numDocs)`
`WorkerRunnable(int numTopics, double[] alpha, double alphaSum, double beta, Randoms random, java.util.ArrayList<TopicAssignment> data, int[][] typeTopicCounts, int[] tokensPerTopic, int startDoc, int numDocs)`

Uses of Randoms in cc.mallet.types

Methods in cc.mallet.types with parameters of type Randoms
Modifier and Type	Method	Description
`void`	InstanceList.`hideSomeLabels(double proportionToHide, Randoms r)`
`Dirichlet`	Dirichlet.`randomDirichlet(Randoms r, double averageAlpha)`
`FeatureSequence`	Dirichlet.`randomFeatureSequence(Randoms r, int length)`
`FeatureSequence`	Multinomial.`randomFeatureSequence(Randoms r, int length)`
`FeatureVector`	Dirichlet.`randomFeatureVector(Randoms r, int size)`
`FeatureVector`	Multinomial.`randomFeatureVector(Randoms r, int size)`
`int`	Multinomial.`randomIndex(Randoms r)`
`Multinomial`	Dirichlet.`randomMultinomial(Randoms r)`
`java.lang.Object`	Multinomial.`randomObject(Randoms r)`
`protected double[]`	Dirichlet.`randomRawMultinomial(Randoms r)`
`TokenSequence`	Dirichlet.`randomTokenSequence(Randoms r, int length)`
`double[]`	Dirichlet.`randomVector(Randoms r)`

Constructors in cc.mallet.types with parameters of type Randoms
Constructor	Description
`InstanceList(Randoms r, int vocabSize, int numClasses)`
`InstanceList(Randoms r, Alphabet vocab, java.lang.String[] classNames, int meanInstancesPerLabel)`
`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.

Uses of Randoms in cc.mallet.util

Methods in cc.mallet.util with parameters of type Randoms
Modifier and Type	Method	Description
`static FeatureVector`	MVNormal.`nextFeatureVector(Alphabet alphabet, double[] mean, double[] precision, Randoms random)`
`static double[]`	MVNormal.`nextMVNormal(double[] mean, double[] precision, Randoms random)`	Sample a multivariate normal from a precision matrix (ie inverse covariance matrix)
`static double[][]`	MVNormal.`nextMVNormal(int n, double[] mean, double[] precision, Randoms random)`
`static double[]`	MVNormal.`nextMVNormalPosterior(double[] priorMean, double[] priorPrecisionDiagonal, double[] precision, double[] observedMean, int observations, Randoms random)`
`static double[]`	MVNormal.`nextMVNormalWithCholesky(double[] mean, double[] precisionLowerTriangular, Randoms random)`
`static double[]`	MVNormal.`nextWishart(double[] sqrtScaleMatrix, int dimension, int degreesOfFreedom, Randoms random)`	A Wishart random variate, based on R code by Bill Venables.
`static double[]`	MVNormal.`nextWishartPosterior(double[] scatterMatrix, int observations, double[] priorPrecisionDiagonal, int priorDF, int dimension, Randoms random)`
`static double[]`	MVNormal.`nextZeroSumMVNormalWithCholesky(double[] mean, double[] precisionLowerTriangular, Randoms random)`	Sample a vector x from N(m, (LL')^-1, such that sum_i x_i = 0.