nodes.learning

LinearMapper

case class LinearMapper(x: DenseMatrix[Double], bOpt: Option[DenseVector[Double]] = scala.None, featureScaler: Option[StandardScalerModel] = scala.None) extends Transformer[DenseVector[Double], DenseVector[Double]] with Product with Serializable

Computes A * x + b i.e. a linear map of data using a trained model.

x

trained model

bOpt

optional intercept to add

featureScaler

optional scaler to apply to data before applying the model

Linear Supertypes
Product, Equals, Transformer[DenseVector[Double], DenseVector[Double]], Pipeline[DenseVector[Double], DenseVector[Double]], TransformerNode[DenseVector[Double]], Serializable, Serializable, Node, AnyRef, Any
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Inherited
  1. LinearMapper
  2. Product
  3. Equals
  4. Transformer
  5. Pipeline
  6. TransformerNode
  7. Serializable
  8. Serializable
  9. Node
  10. AnyRef
  11. Any
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Instance Constructors

  1. new LinearMapper(x: DenseMatrix[Double], bOpt: Option[DenseVector[Double]] = scala.None, featureScaler: Option[StandardScalerModel] = scala.None)

    x

    trained model

    bOpt

    optional intercept to add

    featureScaler

    optional scaler to apply to data before applying the model

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

    Definition Classes
    AnyRef

  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: RDD[DenseVector[Double]]): RDD[DenseVector[Double]]

    Apply a linear model to a collection of inputs.

    Apply a linear model to a collection of inputs.

    in

    Collection of A's.

    returns

    Collection of B's.

    Definition Classes
    LinearMapper → Transformer → Pipeline

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

    Apply a linear model to an input.

    Apply a linear model to an input.

    in

    Input.

    returns

    Output.

    Definition Classes
    LinearMapper → Transformer → Pipeline

  11. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

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

    optional intercept to add

  13. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef

  15. val featureScaler: Option[StandardScalerModel]

    optional scaler to apply to data before applying the model

  16. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    AnyRef → Any

  18. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  19. def label: String

    Definition Classes
    Node

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

    Definition Classes
    AnyRef

  21. final def notify(): Unit

    Definition Classes
    AnyRef

  22. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  24. final def toDOTString: String

    returns

    A graphviz dot representation of this pipeline

    Definition Classes
    Pipeline

  25. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  28. val x: DenseMatrix[Double]

    trained model

Inherited from Product

Inherited from Equals

Inherited from Transformer[DenseVector[Double], DenseVector[Double]]

Inherited from Pipeline[DenseVector[Double], DenseVector[Double]]

Inherited from TransformerNode[DenseVector[Double]]

Inherited from Serializable

Inherited from Serializable

Inherited from Node

Inherited from AnyRef

Inherited from Any

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