Uses of Class
cc.mallet.util.Randoms

Packages that use Randoms
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. 
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
protected  Randoms PairSampleIterator.random
           
 

Methods in cc.mallet.cluster.iterator with parameters of type Randoms
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
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
RandomEvaluator(Randoms random)
           
 

Uses of Randoms in cc.mallet.cluster.util
 

Methods in cc.mallet.cluster.util with parameters of type Randoms
static Clustering ClusterUtils.createRandomClustering(InstanceList instances, Randoms random)
           
 

Uses of Randoms in cc.mallet.grmm.inference
 

Methods in cc.mallet.grmm.inference with parameters of type Randoms
static FactorGraph RandomGraphs.createRandomChain(Randoms r, int length)
           
 void Sampler.setRandom(Randoms r)
          Sets the random seed used by this sampler.
 void GibbsSampler.setRandom(Randoms r)
           
 void ExactSampler.setRandom(Randoms r)
           
 

Constructors in cc.mallet.grmm.inference with parameters of type Randoms
ExactSampler(Randoms r)
           
GibbsSampler(Randoms r, int burnin)
           
 

Uses of Randoms in cc.mallet.grmm.types
 

Methods in cc.mallet.grmm.types with parameters of type Randoms
 Assignment UniNormalFactor.sample(Randoms r)
           
 Assignment UniformFactor.sample(Randoms r)
           
 Assignment SkeletonFactor.sample(Randoms r)
           
 Assignment PottsTableFactor.sample(Randoms r)
           
 Assignment NormalFactor.sample(Randoms r)
           
 Assignment FactorGraph.sample(Randoms r)
           
 Assignment Factor.sample(Randoms r)
          Return an assignment sampled from this factor, interpreting it as an unnormalized probability distribution.
 Assignment CPT.sample(Randoms r)
           
 Assignment ConstantFactor.sample(Randoms r)
           
 Assignment BinaryUnaryFactor.sample(Randoms r)
           
 Assignment BetaFactor.sample(Randoms r)
           
 Assignment Assignment.sample(Randoms r)
           
 Assignment AbstractTableFactor.sample(Randoms r)
           
 Assignment AbstractFactor.sample(Randoms r)
           
 Assignment FactorGraph.sampleContinuousVars(Randoms r)
          Samples the continuous variables in this factor graph.
 int DiscreteFactor.sampleLocation(Randoms r)
           
 int CPT.sampleLocation(Randoms r)
           
 int AbstractTableFactor.sampleLocation(Randoms r)
           
 

Uses of Randoms in cc.mallet.pipe.iterator
 

Constructors in cc.mallet.pipe.iterator with parameters of type Randoms
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)
           
RandomFeatureVectorIterator(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)
           
RandomTokenSequenceIterator(Randoms r, int vocabSize, int numClasses)
           
 

Uses of Randoms in cc.mallet.topics
 

Fields in cc.mallet.topics declared as Randoms
protected  Randoms WorkerRunnable.random
           
protected  Randoms TopicInferencer.random
           
protected  Randoms SimpleLDA.random
           
protected  Randoms PolylingualTopicModel.random
           
protected  Randoms NPTopicModel.random
           
protected  Randoms MarginalProbEstimator.random
           
protected  Randoms LDAHyper.random
          Deprecated.  
 

Methods in cc.mallet.topics with parameters of type Randoms
 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 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 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)
           
 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
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)
           
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)
           
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
 void InstanceList.hideSomeLabels(double proportionToHide, Randoms r)
           
 Dirichlet Dirichlet.randomDirichlet(Randoms r, double averageAlpha)
           
 FeatureSequence Multinomial.randomFeatureSequence(Randoms r, int length)
           
 FeatureSequence Dirichlet.randomFeatureSequence(Randoms r, int length)
           
 FeatureVector Multinomial.randomFeatureVector(Randoms r, int size)
           
 FeatureVector Dirichlet.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
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 FeatureVectors.
InstanceList(Randoms r, int vocabSize, int numClasses)
           
 

Uses of Randoms in cc.mallet.util
 

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