edu.stanford.nlp.tmt

stage

package stage

Visibility
  1. Public
  2. All

Type Members

  1. case class GroupedTopTerms [Grouping] (topic: String, grouping: Grouping, terms: List[String]) extends Product with Serializable

    Describes the top-k terms in a topic by grouping.

  2. case class GroupedTopicUsage [Grouping] (topic: String, grouping: Grouping, numDocuments: Double, numWords: Double) extends Product with Serializable

    Describes the usage of a topic within a grouping.

  3. case class TopTerms (topic: String, terms: List[String]) extends Product with Serializable

    Describes the top-k terms in a topic.

  4. case class TopicUsage (topic: String, numDocuments: Double, numWords: Double) extends Product with Serializable

    Describes the usage of a topic within a grouping.

Value Members

  1. def EstimateLabeledLDAPerWordTopicDistributions (model: edu.stanford.nlp.tmt.model.llda.LabeledLDA[_, _, _], dataset: Iterable[LabeledLDADocumentParams], perDocTopicDistributions: Iterable[(String, SparseArray[Double])]): LazyIterable[(String, Array[SparseArray[Double]])]

    Estimates the per-word topic distributions using the given model counts and the per-document topic distributions.

    Estimates the per-word topic distributions using the given model counts and the per-document topic distributions. This is not as exact as inference, but is nearly so, and is much faster.

    Definition Classes
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  2. def EstimatePerWordTopicDistributions (model: edu.stanford.nlp.tmt.model.lda.LDA[_, _, _], dataset: Iterable[LDADocumentParams], perDocTopicDistributions: Iterable[(String, Array[Double])]): LazyIterable[(String, Array[Array[Double]])]

    Estimates the per-word topic distributions using the given model counts and the per-document topic distributions.

    Estimates the per-word topic distributions using the given model counts and the per-document topic distributions. This is not as exact as inference, but is nearly so, and is much faster.

    Definition Classes
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  3. object GroupedTopTerms extends Serializable

  4. object GroupedTopicUsage extends Serializable

  5. def InferCVB0DocumentTopicDistributions (model: CVB0LDA, dataset: Iterable[LDADocumentParams]): LazyIterable[(String, Array[Double])]

    Returns an array of per-topic probabilities.

    Returns an array of per-topic probabilities.

    Definition Classes
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  6. def InferCVB0LabeledLDADocumentTopicDistributions (model: CVB0LabeledLDA, dataset: Iterable[LabeledLDADocumentParams]): LazyIterable[(String, Array[Double])]

    Returns an array of per-topic probabilities.

    Returns an array of per-topic probabilities.

    Definition Classes
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  7. def InferCVB0PLDADocumentTopicDistributions (model: CVB0PLDA, dataset: Iterable[PLDADocumentParams]): LazyIterable[(String, Array[Double])]

    Returns an array of per-topic probabilities.

    Returns an array of per-topic probabilities.

    Definition Classes
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  8. def InferGibbsDocumentTopicDistributions (model: GibbsLDA, dataset: Iterable[LDADocumentParams]): LazyIterable[(String, Array[Double])]

    Returns an array of per-topic probabilities.

    Returns an array of per-topic probabilities.

    Definition Classes
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  9. def InferGibbsLabeledLDADocumentTopicDistributions (model: GibbsLabeledLDA, dataset: Iterable[LabeledLDADocumentParams]): LazyIterable[(String, Array[Double])]

    Returns an array of per-topic probabilities.

    Returns an array of per-topic probabilities.

    Definition Classes
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  10. def LoadCVB0LDA (path: File): CVB0LDA

    Loads a CVB0LDA model from the given path.

    Loads a CVB0LDA model from the given path.

    Definition Classes
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  11. def LoadCVB0LabeledLDA (path: File): CVB0LabeledLDA

    Loads a CVB0LabeledLDA model from the given path.

    Loads a CVB0LabeledLDA model from the given path.

    Definition Classes
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  12. def LoadCVB0PLDA (path: File): CVB0PLDA

    Loads a CVB0PLDA model from the given path.

    Loads a CVB0PLDA model from the given path.

    Definition Classes
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  13. def LoadGibbsLDA (path: File): GibbsLDA

    Loads a GibbsLDA model from the given path.

    Loads a GibbsLDA model from the given path.

    Definition Classes
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  14. def LoadGibbsLabeledLDA (path: File): GibbsLabeledLDA

    Loads a GibbsLabeledLDA model from the given path.

    Loads a GibbsLabeledLDA model from the given path.

    Definition Classes
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  15. def LoadLDADocumentTopicDistributions (path: CSVFile): LazyIterable[(String, Array[Double])]

    Loads the document-topic assignments from the given path.

    Loads the document-topic assignments from the given path.

    Definition Classes
    package

  16. def QueryTopTerms [ID, Dist, Grouping] (model: edu.stanford.nlp.tmt.model.TopicModel[_, _, _, _, _] with ClosedTopicSet, dataset: Iterable[LDADocumentParams], perDocWordTopicDistribution: Iterable[(String, Array[Dist])], numTopTerms: Int, grouping: Iterable[Item[ID, Grouping]])(implicit active: CanGetActiveValues[Dist, Int, Double], ordering: Ordering[Grouping]): Iterable[GroupedTopTerms[Grouping]]

    Gets the top terms in each topic, but counting terms instances separately for members of each group.

    Gets the top terms in each topic, but counting terms instances separately for members of each group.

    Definition Classes
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  17. def QueryTopTerms [ID, Dist] (model: edu.stanford.nlp.tmt.model.TopicModel[_, _, _, _, _] with ClosedTopicSet, dataset: Iterable[LDADocumentParams], perDocWordTopicDistribution: Iterable[(String, Array[Dist])], numTopTerms: Int)(implicit active: CanGetActiveValues[Dist, Int, Double]): Iterable[TopTerms]

    Gets the top terms in each topic, but counting terms instances separately for members of each group.

    Gets the top terms in each topic, but counting terms instances separately for members of each group.

    Definition Classes
    package

  18. def QueryTopTerms (model: edu.stanford.nlp.tmt.model.TopicModel[_, _, _, _, _] with ClosedTopicSet, numTerms: Int): LazyIterable[(String, String, Int, Double)]

    Returns the top terms associated with the model.

    Returns the top terms associated with the model.

    Definition Classes
    package

  19. def QueryTopicUsage [ID, Grouping] (model: edu.stanford.nlp.tmt.model.TopicModel[_, _, _, _, _] with ClosedTopicSet, dataset: Iterable[LDADocumentParams], perDocTopicDistribution: Iterable[(String, Array[Double])], grouping: Iterable[Item[ID, Grouping]])(implicit active: CanGetActiveValues[Array[Double], Int, Double], ordering: Ordering[Grouping]): Iterable[GroupedTopicUsage[Grouping]]

    Gets the usage of each topic by sub-group.

    Gets the usage of each topic by sub-group.

    Definition Classes
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  20. def QueryTopicUsage [ID] (model: edu.stanford.nlp.tmt.model.TopicModel[_, _, _, _, _] with ClosedTopicSet, dataset: Iterable[LDADocumentParams], perDocTopicDistribution: Iterable[(String, Array[Double])])(implicit active: CanGetActiveValues[Array[Double], Int, Double]): Iterable[TopicUsage]

    Gets the usage of each topic overall within a corpus.

    Gets the usage of each topic overall within a corpus.

    Definition Classes
    package

  21. object TopTerms extends TableRowCompanion[TopTerms, (String, List[String])] with Serializable

  22. object TopicUsage extends TableRowCompanion[TopicUsage, (String, Double, Double)] with Serializable

  23. def TrainCVB0LDA (modelParams: LDAModelParams, dataset: Iterable[LDADocumentParams], output: File, maxIterations: Int = 1000): CVB0LDA

    Trains a CVB0LDA model using the given model parametesr.

    Trains a CVB0LDA model using the given model parametesr.

    Definition Classes
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  24. def TrainCVB0LabeledLDA (modelParams: LabeledLDAModelParams, dataset: Iterable[LabeledLDADocumentParams], output: File, maxIterations: Int = 1000): CVB0LabeledLDA

    Trains a CVB0LabeledLDA model using the given model parametesr.

    Trains a CVB0LabeledLDA model using the given model parametesr.

    Definition Classes
    package

  25. def TrainCVB0PLDA (modelParams: PLDAModelParams, dataset: Iterable[PLDADocumentParams], output: File, maxIterations: Int = 1000): CVB0PLDA

    Trains a CVB0PLDA model using the given model parametesr.

    Trains a CVB0PLDA model using the given model parametesr.

    Definition Classes
    package

  26. def TrainGibbsLDA (modelParams: LDAModelParams, dataset: Iterable[LDADocumentParams], output: File, maxIterations: Int = 1500): GibbsLDA

    Trains a GibbsLDA model using the given model parametesr.

    Trains a GibbsLDA model using the given model parametesr.

    Definition Classes
    package

  27. def TrainGibbsLabeledLDA (modelParams: LabeledLDAModelParams, dataset: Iterable[LabeledLDADocumentParams], output: File, maxIterations: Int = 1500): GibbsLabeledLDA

    Trains a GibbsLabeledLDA model using the given model parametesr.

    Trains a GibbsLabeledLDA model using the given model parametesr.

    Definition Classes
    package