Package cc.mallet.topics

Interface Summary

Interface	Description
TopicReports

Class Summary

Class	Description
AbstractTopicReports
CopyCallable	This task copies topic-word counts from a global array to a local thread-specific array.
DMRCallable	A parallel Dirichlet-multinomial regression topic model runnable task.
DMRInferencer
DMROptimizable
DMRTopicModel
HierarchicalLDA
HierarchicalPAM	Hierarchical PAM, where each node in the DAG has a distribution over all topics on the next level and one additional "node-specific" topic.
JSONTopicReports
LabeledLDA	LabeledLDA
LDA	Deprecated. Use ParallelTopicModel instead.
LDAHyper	Deprecated. Use ParallelTopicModel instead, which uses substantially faster data structures even for non-parallel operation.
LDAStream
MarginalProbEstimator	An implementation of topic model marginal probability estimators presented in Wallach et al., "Evaluation Methods for Topic Models", ICML (2009)
MergeCallable	This task copies topic-word counts from a global array to a local thread-specific array.
MultinomialHMM	Latent Dirichlet Allocation.
NonNegativeMatrixFactorization
NPTopicModel	A non-parametric topic model that uses the "minimal path" assumption to reduce bookkeeping.
PAM4L	Four Level Pachinko Allocation with MLE learning, based on Andrew's Latent Dirichlet Allocation.
ParallelTopicModel	Simple parallel threaded implementation of LDA, following Newman, Asuncion, Smyth and Welling, Distributed Algorithms for Topic Models JMLR (2009), with SparseLDA sampling scheme and data structure from Yao, Mimno and McCallum, Efficient Methods for Topic Model Inference on Streaming Document Collections, KDD (2009).
PolylingualTopicModel	Latent Dirichlet Allocation for loosely parallel corpora in arbitrary languages
RTopicModel	A wrapper for a topic model to be used from the R statistical package through rJava.
SimpleLDA	A simple implementation of Latent Dirichlet Allocation using Gibbs sampling.
TopicalNGrams	Like Latent Dirichlet Allocation, but with integrated phrase discovery.
TopicAssignment	This class combines a sequence of observed features with a sequence of hidden "labels".
TopicInferencer
TopicModelDiagnostics
WeightedTopicModel
WordEmbeddingCallable
WordEmbeddings
WorkerCallable	A parallel topic model callable task.
WorkerRunnable	A parallel topic model runnable task.