ScalaLDAvis

Value Members

1. final def !=(arg0: Any): Boolean

   

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2. final def ##(): Int

   

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3. final def ==(arg0: Any): Boolean

   

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4. final def asInstanceOf[T0]: T0

   

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5. def clone(): AnyRef

   

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   @throws( ... )

6. val cvModel: CountVectorizerModel

   

7. val cvModelPath: String

   

8. val docLengths: DenseVector[Double]

   

9. val docTopicDist: RDD[Double]

   

10. val docTopicDistMat: DenseMatrix[Double]

    

11. final def eq(arg0: AnyRef): Boolean

    

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12. def equals(arg0: Any): Boolean

    

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13. def finalize(): Unit

    

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14. final def getClass(): Class[_]

    

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15. val getSize: (WrappedArray[String]) ⇒ Long

    

16. def getTokenTable(termIndex: Array[Int], termTopicFreq: DenseMatrix[Double], vocab: Array[String], termFrequency: DenseVector[Double]): Dataset[TokenTable]

    

17. def getTopicCoordinates(topicTermDist: DenseMatrix[Double], topicProportion: Array[(Double, Int)]): Dataset[TopicCoordinates]

    

18. def getTopicInfo(topicTermDists: DenseMatrix[Double], topicProportion: DenseVector[Double], termFrequency: DenseVector[Double], termTopicFreq: DenseMatrix[Double], vocab: Array[String], topicOrder: Array[Int], lambdaStep: Double = 0.01, R: Int = 30, nJobs: Int = 0): (DataFrame, Array[Int])

    

19. def hashCode(): Int

    

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20. final def isInstanceOf[T0]: Boolean

    

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21. val kDim: Int

    

22. val ldaModel: LDAModel

    

23. val ldaModelPath: String

    

24. def loadDF(path: String): DataFrame

    

25. final def ne(arg0: AnyRef): Boolean

    

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26. final def notify(): Unit

    

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27. final def notifyAll(): Unit

    

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28. def prepareLDAVisData(path: String, lambdaStep: Double = 0.01, plotOpts: Map[String, String] = ..., R: Int = 30): Unit

    

    Transforms the topic model distributions and related corpus data into the data structures needed for the visualization.

    Transforms the topic model distributions and related corpus data into the data structures needed for the visualization.

    path

    Directory to store the visulaization data

    lambdaStep

    Determines the interstep distance in the grid of lambda values over which to iterate when computing relevance. Default is 0.01. Recommended to be between 0.01 and 0.1.

    plotOpts

    Dictionary of plotting options, right now only used for the axis labels.

    R

    The number of terms to display in the barcharts of the visualization. Default is 30. Recommended to be roughly between 10 and 50. Notes ----- This implements the method of Sievert, C. and Shirley, K. (2014): LDAvis: A Method for Visualizing and Interpreting Topics, ACL Workshop on Interactive Language Learning, Visualization, and Interfaces. http://nlp.stanford.edu/events/illvi2014/papers/sievert-illvi2014.pdf

    Definition Classes

    ScalaLDAvis → LDAvis

29. val sc: SparkContext

    

30. val sizeUdf: UserDefinedFunction

    

31. val spark: SparkSession

    

32. final def synchronized[T0](arg0: ⇒ T0): T0

    

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33. def toString(): String

    

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34. val topicsTermDist: DenseMatrix[Double]

    

35. var transformedDF: DataFrame

    

36. val transformedDFFiltered: Dataset[Row]

    

37. val transformedDfPath: String

    

38. val vocab: Array[String]

    

39. val vocabDf: DataFrame

    

40. val vocabSize: Int

    

41. final def wait(): Unit

    

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42. final def wait(arg0: Long, arg1: Int): Unit

    

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43. final def wait(arg0: Long): Unit

    

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44. val wordsDim: Int