LinearMapEstimator

Value Members

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

   Definition Classes

   AnyRef

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

   Definition Classes

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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

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6. final def asInstanceOf[T0]: T0

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7. def clone(): AnyRef

   Attributes

   protected[java.lang]

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   @throws( ... )

8. def cost(n: Long, d: Int, k: Int, sparsity: Double, numMachines: Int, cpuWeight: Double, memWeight: Double, networkWeight: Double): Double

   Definition Classes

   LinearMapEstimator → CostModel

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

   Definition Classes

   AnyRef

10. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

11. def execute(deps: Seq[Expression]): TransformerExpression

    Definition Classes

    EstimatorOperator → Operator

12. def finalize(): Unit

    Attributes

    protected[java.lang]

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    @throws( classOf[java.lang.Throwable] )

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

    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

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

    Definition Classes

    AnyRef → Any

15. def hashCode(): Int

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16. final def isInstanceOf[T0]: Boolean

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17. def label: String

    Definition Classes

    Operator

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

    Definition Classes

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19. final def notify(): Unit

    Definition Classes

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20. final def notifyAll(): Unit

    Definition Classes

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

    Definition Classes

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22. def toString(): String

    Definition Classes

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23. final def wait(): Unit

    Definition Classes

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    @throws( ... )

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

    Definition Classes

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

    Definition Classes

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26. final def withData(data: PipelineDataset[DenseVector[Double]], labels: PipelineDataset[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes

    LabelEstimator

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

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes

    LabelEstimator

28. final def withData(data: PipelineDataset[DenseVector[Double]], labels: RDD[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes

    LabelEstimator

29. final def withData(data: RDD[DenseVector[Double]], labels: PipelineDataset[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    Constructs a pipeline that fits this label estimator to training data and labels, then applies the resultant transformer to the Pipeline input.

    data

    The training data

    labels

    The training labels

    returns

    A pipeline that fits this label estimator and applies the result to inputs.

    Definition Classes

    LabelEstimator