RichVectorFeature

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]

   

   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]

   

   FeatureLike

10. def filterMinVariance(minVariance: Double = MinVarianceFilter.MinVariance, removeBadFeatures: Boolean = MinVarianceFilter.RemoveBadFeatures): FeatureLike[OPVector]

    

    Apply filter that removes computed features that have variance <= minVariance

11. def finalize(): Unit

    

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

    

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13. def hashCode(): Int

    

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

    

    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)

15. final def isInstanceOf[T0]: Boolean

    

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16. 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]

    

    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

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

    

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18. final def notify(): Unit

    

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19. final def notifyAll(): Unit

    

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20. 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]

    

    Apply Random Forest classifier

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

    

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22. def toString(): String

    

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23. final def wait(): Unit

    

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

    

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

    

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