org.apache.spark.mllib.clustering

LDAModel

abstract class LDAModel extends AnyRef

:: Experimental ::

Latent Dirichlet Allocation (LDA) model.

This abstraction permits for different underlying representations, including local and distributed data structures.

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  1. abstract def describeTopics(maxTermsPerTopic: Int): Array[(Array[Int], Array[Double])]

    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.

    maxTermsPerTopic

    Maximum number of terms to collect for each topic.

    returns

    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.

  2. abstract def k: Int

    Number of topics

  3. abstract def topicsMatrix: Matrix

    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.

  4. abstract def vocabSize: Int

    Vocabulary size (number of terms or terms in the vocabulary)

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  8. def describeTopics(): Array[(Array[Int], Array[Double])]

    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!

    returns

    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.

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