nodes.learning

LinearMapEstimator

class LinearMapEstimator extends LabelEstimator[DenseVector[Double], DenseVector[Double], DenseVector[Double]]

Linear Map Estimator. Solves an OLS problem on data given labels and emits a LinearMapper transformer.

Linear Supertypes
LabelEstimator[DenseVector[Double], DenseVector[Double], DenseVector[Double]], EstimatorNode, Serializable, Serializable, Node, AnyRef, Any
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  1. LinearMapEstimator
  2. LabelEstimator
  3. EstimatorNode
  4. Serializable
  5. Serializable
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Instance Constructors

  1. new LinearMapEstimator(lambda: Option[Double] = scala.None)

    lambda

    L2 Regularization parameter

Value Members

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

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

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  3. final def ##(): Int

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  7. def clone(): AnyRef

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    protected[java.lang]
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  8. final def eq(arg0: AnyRef): Boolean

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  9. def equals(arg0: Any): Boolean

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  10. def finalize(): Unit

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  11. def fit(trainingFeatures: RDD[DenseVector[Double]], trainingLabels: RDD[DenseVector[Double]]): LinearMapper

    Learns a linear model (OLS) based on training features and training labels.

    Learns a linear model (OLS) based on training features and training labels. If the regularization parameter is set

    trainingFeatures

    Training features.

    trainingLabels

    Training labels.

    returns

    Definition Classes
    LinearMapEstimator → LabelEstimator

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

    Definition Classes
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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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  15. def label: String

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    Node

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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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

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  20. def toString(): String

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  21. final def wait(): Unit

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

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

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  24. def withData(data: RDD[DenseVector[Double]], labels: RDD[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline from a single label estimator and training data.

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

    data

    The training data

    labels

    The training labels

    Definition Classes
    LabelEstimator

Inherited from LabelEstimator[DenseVector[Double], DenseVector[Double], DenseVector[Double]]

Inherited from EstimatorNode

Inherited from Serializable

Inherited from Serializable

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