Class

com.salesforce.op.dsl.RichVectorFeature

RichVectorFeature

Related Doc: package RichVectorFeature

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implicit class RichVectorFeature extends AnyRef

Enrichment functions for Vector Feature

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Instance Constructors

  1. new RichVectorFeature(f: FeatureLike[OPVector])

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    f

    FeatureLike

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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  6. def dropIndicesBy(matchFn: (OpVectorColumnMetadata) ⇒ Boolean): FeatureLike[OPVector]

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    Allows columns to be dropped from a feature vector based on properties of the metadata about what is contained in each column (will work only on vectors) created with OpVectorMetadata

    Allows columns to be dropped from a feature vector based on properties of the metadata about what is contained in each column (will work only on vectors) created with OpVectorMetadata

    matchFn

    function that goes from OpVectorColumnMetadata to boolean for dropping columns (cases that evaluate to true will be dropped)

    returns

    new Vector with columns removed by function

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

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  8. def equals(arg0: Any): Boolean

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  9. val f: FeatureLike[OPVector]

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    FeatureLike

  10. def finalize(): Unit

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

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  12. def hashCode(): Int

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  13. def idf(minDocFreq: Int = 0): FeatureLike[OPVector]

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    Apply inverse-document frequency transformation.

    Apply inverse-document frequency transformation.

    minDocFreq

    minimum number of documents in which a term should appear for filtering (default: 0)

  14. final def isInstanceOf[T0]: Boolean

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  15. def lda(checkpointInterval: Int = 10, k: Int = 10, maxIter: Int = 10, optimizer: String = "online", subsamplingRate: Double = 0.05, seed: Long = util.Random.nextLong()): FeatureLike[OPVector]

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    Apply Latent Dirichlet Allocation to compute topic distributions

    Apply Latent Dirichlet Allocation to compute topic distributions

    checkpointInterval

    num of iterations between two consecutive checkpoints, -1 means disabled

    k

    number of topics (clusters) to infer

    maxIter

    maximum number of iterations

    optimizer

    optimizer or inference algorithm used to estimate the LDA model, "online" or "em"

    subsamplingRate

    fraction of the corpus to be sampled and used in mini-batch gradient descent

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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. def randomForest(label: FeatureLike[RealNN], maxDepth: Int = 5, maxBins: Int = 32, minInstancePerNode: Int = 1, minInfoGain: Double = 0.0, subSamplingRate: Double = 1.0, numTrees: Int = 20, impurity: Impurity = Impurity.Entropy, seed: Long = util.Random.nextLong, thresholds: Array[Double] = Array.empty): FeatureLike[Prediction]

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    Apply Random Forest classifier

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

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  21. def toString(): String

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  22. final def wait(): Unit

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

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

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