nodes.util

AllSparseFeatures

case class AllSparseFeatures[T]()(implicit evidence$1: ClassTag[T]) extends Estimator[Seq[(T, Double)], SparseVector[Double]] with Product with Serializable

An Estimator that chooses all sparse features observed when training, and produces a transformer which builds a sparse vector out of them.

Deterministically orders the feature mappings by earliest appearance in the RDD

Linear Supertypes
Product, Equals, Estimator[Seq[(T, Double)], SparseVector[Double]], EstimatorNode, Serializable, Serializable, Node, AnyRef, Any
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Inherited
  1. AllSparseFeatures
  2. Product
  3. Equals
  4. Estimator
  5. EstimatorNode
  6. Serializable
  7. Serializable
  8. Node
  9. AnyRef
  10. Any
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Instance Constructors

  1. new AllSparseFeatures()(implicit arg0: ClassTag[T])

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 asInstanceOf[T0]: T0

    Definition Classes
    Any

  7. def clone(): AnyRef

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

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

    Definition Classes
    AnyRef

  9. def finalize(): Unit

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

  10. def fit(data: RDD[Seq[(T, Double)]]): SparseFeatureVectorizer[T]

    An estimator has a fit method which emits a Transformer.

    An estimator has a fit method which emits a Transformer.

    data

    Input data.

    returns

    A Transformer which can be called on new data.

    Definition Classes
    AllSparseFeatures → Estimator

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

    Definition Classes
    AnyRef → Any

  12. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  13. def label: String

    Definition Classes
    Node

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

    Definition Classes
    AnyRef

  15. final def notify(): Unit

    Definition Classes
    AnyRef

  16. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  18. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  21. def withData(data: RDD[Seq[(T, Double)]]): Pipeline[Seq[(T, Double)], SparseVector[Double]]

    Constructs a pipeline from a single estimator and training data.

    Constructs a pipeline from a single estimator and training data. Equivalent to Pipeline() andThen (estimator, data)

    data

    The training data

    Definition Classes
    Estimator

Inherited from Product

Inherited from Equals

Inherited from Estimator[Seq[(T, Double)], SparseVector[Double]]

Inherited from EstimatorNode

Inherited from Serializable

Inherited from Serializable

Inherited from Node

Inherited from AnyRef

Inherited from Any

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