org.apache.mahout.classifier.naivebayes

NaiveBayes

object NaiveBayes extends NaiveBayes with Serializable

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  1. type CategoryParser = (String) ⇒ String

    Definition Classes
    NaiveBayes

Value Members

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

    Definition Classes
    AnyRef
  2. final def !=(arg0: Any): Boolean

    Definition Classes
    Any
  3. final def ##(): Int

    Definition Classes
    AnyRef → Any
  4. final def ==(arg0: AnyRef): Boolean

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

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  6. def argmax(v: Vector): (Int, Double)

    argmax with values as well returns a tuple of index of the max score and the score itself.

    argmax with values as well returns a tuple of index of the max score and the score itself.

    v

    Vector of of scores

    returns

    (bestIndex, bestScore)

    Definition Classes
    NaiveBayes
  7. final def asInstanceOf[T0]: T0

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  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
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    @throws( ... )
  9. def defaultAlphaI: Float

    default value for the Laplacian smoothing parameter

    default value for the Laplacian smoothing parameter

    Definition Classes
    NaiveBayes
  10. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  12. def extractLabelsAndAggregateObservations[K](stringKeyedObservations: DrmLike[K], cParser: (String) ⇒ String = seq2SparseCategoryParser)(implicit arg0: ClassTag[K], ctx: DistributedContext): (HashMap[String, Integer], DrmLike[Int])

    Extract label Keys from raw TF or TF-IDF Matrix generated by seqdirectory/seq2sparse and aggregate TF or TF-IDF values by their label Override this method in engine specific modules to optimize

    Extract label Keys from raw TF or TF-IDF Matrix generated by seqdirectory/seq2sparse and aggregate TF or TF-IDF values by their label Override this method in engine specific modules to optimize

    stringKeyedObservations

    DrmLike matrix; Output from seq2sparse in form K = eg./Category/document_title V = TF or TF-IDF values per term

    cParser

    a String => String function used to extract categories from Keys of the stringKeyedObservations DRM. The default CategoryParser will extract "Category" from: '/Category/document_id'

    returns

    (labelIndexMap,aggregatedByLabelObservationDrm) labelIndexMap is a HashMap [String, Integer] K = label row index V = label aggregatedByLabelObservationDrm is a DrmLike[Int] of aggregated TF or TF-IDF counts per label

    Definition Classes
    NaiveBayes
  13. def finalize(): Unit

    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  14. final def getClass(): Class[_]

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  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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

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  18. final def notify(): Unit

    Definition Classes
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  19. final def notifyAll(): Unit

    Definition Classes
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  20. def seq2SparseCategoryParser: (String) ⇒ String

    Default: seqdirectory/seq2Sparse Categories are Stored in Drm Keys as: /Category/document_id

    Default: seqdirectory/seq2Sparse Categories are Stored in Drm Keys as: /Category/document_id

    Definition Classes
    NaiveBayes
  21. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  22. def test[K](model: NBModel, testSet: DrmLike[K], testComplementary: Boolean = false, cParser: (String) ⇒ String = seq2SparseCategoryParser)(implicit arg0: ClassTag[K], ctx: DistributedContext): ResultAnalyzer

    Test a trained model with a labeled dataset sequentially

    Test a trained model with a labeled dataset sequentially

    K

    implicitly determined Key type of test set DRM: String

    model

    a trained NBModel

    testSet

    a labeled testing set

    testComplementary

    test using a complementary or a standard NB classifier

    cParser

    a String => String function used to extract categories from Keys of the testing set DRM. The default CategoryParser will extract "Category" from: '/Category/document_id'

    *Note*: this method brings the entire test set into upfront memory, This method is optimized and parallelized in SparkNaiveBayes

    returns

    a result analyzer with confusion matrix and accuracy statistics

    Definition Classes
    NaiveBayes
  23. def toString(): String

    Definition Classes
    AnyRef → Any
  24. def train(observationsPerLabel: DrmLike[Int], labelIndex: Map[String, Integer], trainComplementary: Boolean = true, alphaI: Float = defaultAlphaI): NBModel

    Distributed training of a Naive Bayes model.

    Distributed training of a Naive Bayes model. Follows the approach presented in Rennie et.al.: Tackling the poor assumptions of Naive Bayes Text classifiers, ICML 2003, http://people.csail.mit.edu/jrennie/papers/icml03-nb.pdf

    observationsPerLabel

    a DrmLike[Int] matrix containing term frequency counts for each label.

    trainComplementary

    whether or not to train a complementary Naive Bayes model

    alphaI

    Laplace smoothing parameter

    returns

    trained naive bayes model

    Definition Classes
    NaiveBayes
  25. final def wait(): Unit

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

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

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Inherited from NaiveBayes

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