com.alpine.model.pack.multiple

PipelineClusteringTransformer

case class PipelineClusteringTransformer(preProcessors: List[_ <: Transformer], finalTransformer: ClusteringTransformer, subModels: Seq[RowModel]) extends PipelineCategoricalTransformer[ClusteringResult] with ClusteringTransformer with Product with Serializable

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Inherited
  1. PipelineClusteringTransformer
  2. Product
  3. Equals
  4. ClusteringTransformer
  5. PipelineCategoricalTransformer
  6. CategoricalTransformer
  7. PipelineMLTransformer
  8. MLTransformer
  9. PipelineTransformer
  10. Transformer
  11. Serializable
  12. Serializable
  13. AnyRef
  14. Any
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Instance Constructors

  1. new PipelineClusteringTransformer(preProcessors: List[_ <: Transformer], finalTransformer: ClusteringTransformer, subModels: Seq[RowModel])

Type Members

  1. type Row = Seq[Any]

    Shorthand for the input / output type of the apply method.

    Shorthand for the input / output type of the apply method. Equivalent to Seq[Any].

    Definition Classes
    Transformer

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. def allowNullValues: Boolean

    Allows the transformer to specify if the apply method can handle null values in the input row.

    Allows the transformer to specify if the apply method can handle null values in the input row.

    e.g. a Null value replacement transformer, or a Naive Bayes transformer would naturally handle null values, but a Linear Regression transformer would not.

    Default value is false.

    returns

    Boolean indicating tolerance for null values in input.

    Definition Classes
    Transformer
  7. def apply(row: Row): Row

    This method should be implemented with speed and garbage collection in mind, as it called once per row on potentially huge data-sets.

    This method should be implemented with speed and garbage collection in mind, as it called once per row on potentially huge data-sets.

    This method is not required to be thread-safe.

    row

    The row of input to be scored.

    returns

    The result from applying the trained model to the row.

    Definition Classes
    PipelineCategoricalTransformerCategoricalTransformerPipelineTransformerTransformer
  8. def apply(row: Row, tList: List[Transformer], reordering: List[Option[Array[Int]]]): Row

    Attributes
    protected
    Definition Classes
    PipelineTransformer
  9. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  10. def classLabels: Seq[String]

    The result must always return the labels in the order specified here.

    The result must always return the labels in the order specified here.

    returns

    The class labels in the order that they will be returned by the result.

    Definition Classes
    PipelineCategoricalTransformerCategoricalTransformer
  11. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  12. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  13. val finalTransformer: ClusteringTransformer

  14. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  15. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  16. def inputRowForLastModel(row: Row): Row

    Attributes
    protected
    Definition Classes
    PipelineMLTransformer
  17. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  18. val lastReordering: Option[Array[Int]]

    Attributes
    protected
    Definition Classes
    PipelineTransformer
  19. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  20. final def notify(): Unit

    Definition Classes
    AnyRef
  21. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  22. val preProcessors: List[_ <: Transformer]

  23. val reorderingIndices: List[Option[Array[Int]]]

    Attributes
    protected
    Definition Classes
    PipelineTransformer
  24. def score(row: Row): ClusteringResult

  25. def scoreDistances(row: Row): Array[Double]

  26. val subModels: Seq[RowModel]

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

    Definition Classes
    AnyRef
  28. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  29. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  30. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Product

Inherited from Equals

Inherited from ClusteringTransformer

Inherited from PipelineTransformer

Inherited from Transformer

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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