keystoneml.nodes.learning

LinearMapper

case class LinearMapper[T <: Vector[Double]](x: DenseMatrix[Double], bOpt: Option[DenseVector[Double]] = scala.None, featureScaler: Option[StandardScalerModel] = scala.None) extends Transformer[T, 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[T, DenseVector[Double]], Chainable[T, DenseVector[Double]], TransformerOperator, Serializable, Serializable, Operator, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. LinearMapper
  2. Product
  3. Equals
  4. Transformer
  5. Chainable
  6. TransformerOperator
  7. Serializable
  8. Serializable
  9. Operator
  10. AnyRef
  11. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

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: PipelineDataset[T], labels: PipelineDataset[L]): Pipeline[T, C]

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

    Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    labels

    The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

    Definition Classes
    Chainable

  7. final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: RDD[T], labels: PipelineDataset[L]): Pipeline[T, C]

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

    Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    labels

    The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

    Definition Classes
    Chainable

  8. final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: PipelineDataset[T], labels: RDD[L]): Pipeline[T, C]

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

    Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    labels

    The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

    Definition Classes
    Chainable

  9. final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: RDD[T], labels: RDD[L]): Pipeline[T, C]

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

    Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    labels

    The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

    Definition Classes
    Chainable

  10. final def andThen[C](est: Estimator[DenseVector[Double], C], data: PipelineDataset[T]): Pipeline[T, C]

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

    Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    Definition Classes
    Chainable

  11. final def andThen[C](est: Estimator[DenseVector[Double], C], data: RDD[T]): Pipeline[T, C]

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

    Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    Definition Classes
    Chainable

  12. final def andThen[C](next: Chainable[DenseVector[Double], C]): Pipeline[T, 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. If either this pipeline or the following has already been executed, it will not need to be fit again.

    next

    the pipeline to chain

    Definition Classes
    Chainable

  13. def apply(in: RDD[T]): 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

  14. def apply(in: T): DenseVector[Double]

    Apply a linear model to an input.

    Apply a linear model to an input.

    in

    Input.

    returns

    Output.

    Definition Classes
    LinearMapper → Transformer

  15. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

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

    optional intercept to add

  17. def clone(): AnyRef

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

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

    Definition Classes
    AnyRef

  19. def execute(deps: Seq[Expression]): Expression

    Definition Classes
    TransformerOperator → Operator

  20. val featureScaler: Option[StandardScalerModel]

    optional scaler to apply to data before applying the model

  21. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  23. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  24. def label: String

    Definition Classes
    Operator

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

    Definition Classes
    AnyRef

  26. final def notify(): Unit

    Definition Classes
    AnyRef

  27. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  29. def toPipeline: Pipeline[T, DenseVector[Double]]

    A method that converts this object into a Pipeline.

    A method that converts this object into a Pipeline. Must be implemented by anything that extends Chainable.

    Definition Classes
    Transformer → Chainable

  30. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  33. val x: DenseMatrix[Double]

    trained model

Inherited from Product

Inherited from Equals

Inherited from Transformer[T, DenseVector[Double]]

Inherited from Chainable[T, DenseVector[Double]]

Inherited from TransformerOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

Ungrouped