org.apache.spark.mllib.clustering
Return the topics described by weighted terms.
Return the topics described by weighted terms.
This limits the number of terms per topic. This is approximate; it may not return exactly the top-weighted terms for each topic. To get a more precise set of top terms, increase maxTermsPerTopic.
Maximum number of terms to collect for each topic.
Array over topics. Each topic is represented as a pair of matching arrays: (term indices, term weights in topic). Each topic's terms are sorted in order of decreasing weight.
Return the topics described by weighted terms.
Return the topics described by weighted terms.
WARNING: If vocabSize and k are large, this can return a large object!
Array over topics. Each topic is represented as a pair of matching arrays: (term indices, term weights in topic). Each topic's terms are sorted in order of decreasing weight.
Number of topics
Number of topics
Inferred topics, where each topic is represented by a distribution over terms.
Inferred topics, where each topic is represented by a distribution over terms. This is a matrix of size vocabSize x k, where each column is a topic. No guarantees are given about the ordering of the topics.
Vocabulary size (number of terms or terms in the vocabulary)
Vocabulary size (number of terms or terms in the vocabulary)
:: Experimental ::
Local LDA model. This model stores only the inferred topics. It may be used for computing topics for new documents, but it may give less accurate answers than the DistributedLDAModel.