LDA

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11. def incrementalTrain(docs: RDD[BOW], computedModel: LocalLDAModel, totalIter: Int, LDAAlgorithm: String, partStrategy: String, chkptInterval: Int, calcPerplexity: Boolean, storageLevel: StorageLevel): DistributedLDAModel

    

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18. def train(docs: RDD[BOW], totalIter: Int, numTopics: Int, alpha: Float, beta: Float, alphaAS: Float, LDAAlgorithm: String, partStrategy: String, chkptInterval: Int, calcPerplexity: Boolean, storageLevel: StorageLevel): DistributedLDAModel

    

    LDA training

    LDA training

    docs

    RDD of documents, which are term (word) count vectors paired with IDs. The term count vectors are "bags of words" with a fixed-size vocabulary (where the vocabulary size is the length of the vector). Document IDs must be unique and >= 0.

    totalIter

    the number of iterations

    numTopics

    the number of topics (5000+ for large data)

    alpha

    recommend to be (5.0 /numTopics)

    beta

    recommend to be in range 0.001 - 0.1

    alphaAS

    recommend to be in range 0.01 - 1.0

    LDAAlgorithm

    which LDA sampling algorithm to use, recommend not lightlda for short text

    partStrategy

    which partition strategy to re partition by the graph

    storageLevel

    StorageLevel that the LDA Model RDD uses

    returns

    DistributedLDAModel

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