Class

keystoneml.nodes.stats

LinearRectifier

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case class LinearRectifier(maxVal: Double = 0.0, alpha: Double = 0.0) extends Transformer[DenseVector[Double], DenseVector[Double]] with Product with Serializable

This transformer applies a Linear Rectifier, an activation function defined as: f(x) = max({@param maxVal}, x - {@param alpha})

Linear Supertypes
Product, Equals, Transformer[DenseVector[Double], DenseVector[Double]], Chainable[DenseVector[Double], DenseVector[Double]], TransformerOperator, Serializable, Serializable, Operator, AnyRef, Any
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  1. LinearRectifier
  2. Product
  3. Equals
  4. Transformer
  5. Chainable
  6. TransformerOperator
  7. Serializable
  8. Serializable
  9. Operator
  10. AnyRef
  11. Any
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Instance Constructors

  1. new LinearRectifier(maxVal: Double = 0.0, alpha: Double = 0.0)

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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

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

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    Definition Classes
    AnyRef → Any

  4. val alpha: Double

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

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    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

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

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    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: PipelineDataset[DenseVector[Double]], labels: RDD[L]): Pipeline[DenseVector[Double], C]

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    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: RDD[DenseVector[Double]], labels: RDD[L]): Pipeline[DenseVector[Double], C]

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    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](est: Estimator[DenseVector[Double], C], data: PipelineDataset[DenseVector[Double]]): Pipeline[DenseVector[Double], C]

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    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

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

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    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](next: Chainable[DenseVector[Double], C]): Pipeline[DenseVector[Double], C]

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    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

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

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    The application of this Transformer to a single input item.

    The application of this Transformer to a single input item. This method MUST be overridden by ML developers.

    in

    The input item to pass into this transformer

    returns

    The output value

    Definition Classes
    LinearRectifier → Transformer

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

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    The application of this Transformer to an RDD of input items.

    The application of this Transformer to an RDD of input items. This method may optionally be overridden by ML developers.

    in

    The bulk RDD input to pass into this transformer

    returns

    The bulk RDD output for the given input

    Definition Classes
    Transformer

  14. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  15. def clone(): AnyRef

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

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

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    Definition Classes
    AnyRef

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

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    Definition Classes
    TransformerOperator → Operator

  18. def finalize(): Unit

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

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

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    Definition Classes
    AnyRef → Any

  20. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  21. def label: String

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    Definition Classes
    Operator

  22. val maxVal: Double

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

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    Definition Classes
    AnyRef

  24. final def notify(): Unit

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    Definition Classes
    AnyRef

  25. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

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

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    Definition Classes
    AnyRef

  27. def toPipeline: Pipeline[DenseVector[Double], DenseVector[Double]]

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    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

  28. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Product

Inherited from Equals

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

Inherited from Chainable[DenseVector[Double], DenseVector[Double]]

Inherited from TransformerOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

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