BlockLinearMapper

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

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4. final def ==(arg0: AnyRef): Boolean

   Definition Classes

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

   Definition Classes

   Any

6. final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: RDD[DenseVector[Double]], labels: RDD[L]): PipelineWithFittedTransformer[DenseVector[Double], DenseVector[Double], C]

   Definition Classes

   Pipeline

7. final def andThen[C](est: Estimator[DenseVector[Double], C], data: RDD[DenseVector[Double]]): PipelineWithFittedTransformer[DenseVector[Double], DenseVector[Double], C]

   Definition Classes

   Pipeline

8. final def andThen[C](next: Pipeline[DenseVector[Double], C]): Pipeline[DenseVector[Double], C]

   Chains a pipeline onto the end of this one, producing a new pipeline.

   Chains a pipeline onto the end of this one, producing a new pipeline.

   next

   the pipeline to chain

   Definition Classes

   Pipeline

9. def apply(in: DenseVector[Double]): DenseVector[Double]

   Apply this Transformer to a single input item

   Apply this Transformer to a single input item

   in

   The input item to pass into this transformer

   returns

   The output value

   Definition Classes

   BlockLinearMapper → Transformer → Pipeline

10. def apply(in: Seq[RDD[DenseVector[Double]]]): RDD[DenseVector[Double]]

    Applies the linear model to feature vectors large enough to have been split into several RDDs.

    Applies the linear model to feature vectors large enough to have been split into several RDDs.

    returns

    the output vectors

11. def apply(in: RDD[DenseVector[Double]]): RDD[DenseVector[Double]]

    Applies the linear model to feature vectors large enough to have been split into several RDDs.

    Applies the linear model to feature vectors large enough to have been split into several RDDs.

    in

    RDD of vectors to apply the model to

    returns

    the output vectors

    Definition Classes

    BlockLinearMapper → Transformer → Pipeline

12. def applyAndEvaluate(in: Seq[RDD[DenseVector[Double]]], evaluator: (RDD[DenseVector[Double]]) ⇒ Unit): Unit

    Applies the linear model to feature vectors.

    Applies the linear model to feature vectors. After processing chunk i of every vector, applies

    in

    sequence of input RDD chunks

    evaluator

    to the intermediate output vector.

13. def applyAndEvaluate(in: RDD[DenseVector[Double]], evaluator: (RDD[DenseVector[Double]]) ⇒ Unit): Unit

    Applies the linear model to feature vectors.

    Applies the linear model to feature vectors. After processing chunk i of every vector, applies

    in

    input RDD

    evaluator

    to the intermediate output vector.

14. final def asInstanceOf[T0]: T0

    Definition Classes

    Any

15. val bOpt: Option[DenseVector[Double]]

    optional intercept term to be added

16. val blockSize: Int

    blockSize to split data before applying transformations

17. def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

19. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

20. val featureScalers: Seq[Transformer[DenseVector[Double], DenseVector[Double]]]

21. val featureScalersOpt: Option[Seq[Transformer[DenseVector[Double], DenseVector[Double]]]]

    optional seq of transformers to be applied before transformation

22. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes

    AnyRef → Any

24. def hashCode(): Int

    Definition Classes

    AnyRef → Any

25. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

26. def label: String

    Definition Classes

    Node

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

    Definition Classes

    AnyRef

28. final def notify(): Unit

    Definition Classes

    AnyRef

29. final def notifyAll(): Unit

    Definition Classes

    AnyRef

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

    Definition Classes

    AnyRef

31. final def toDOTString: String

    returns

    A graphviz dot representation of this pipeline

    Definition Classes

    Pipeline

32. def toString(): String

    Definition Classes

    AnyRef → Any

33. val vectorSplitter: VectorSplitter

34. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

37. val xs: Seq[DenseMatrix[Double]]

    The chunks of the matrix representing the linear model