SparseLBFGSwithL2

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. val convergenceTol: Double

   convergence tolerance for L-BFGS

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

   Definition Classes

   SparseLBFGSwithL2 → CostModel

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

    Definition Classes

    AnyRef

11. def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

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

    Definition Classes

    EstimatorOperator → Operator

13. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

14. def fit(data: RDD[SparseVector[Double]], labels: RDD[DenseVector[Double]]): SparseLinearMapper

    The type-safe method that ML developers need to implement when writing new Estimators.

    The type-safe method that ML developers need to implement when writing new Estimators.

    data

    The estimator's training data.

    labels

    The estimator's training labels

    returns

    A new transformer

    Definition Classes

    SparseLBFGSwithL2 → LabelEstimator

15. val fitIntercept: Boolean

    Whether to fit the intercepts or not.

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

    Definition Classes

    AnyRef → Any

17. val gradient: SparseGradient

    Gradient function to be used.

18. def hashCode(): Int

    Definition Classes

    AnyRef → Any

19. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

20. def label: String

    Definition Classes

    Operator

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

    Definition Classes

    AnyRef

22. final def notify(): Unit

    Definition Classes

    AnyRef

23. final def notifyAll(): Unit

    Definition Classes

    AnyRef

24. val numCorrections: Int

    3 < numCorrections < 10 is recommended.

25. val numIterations: Int

    max number of iterations to run

26. val regParam: Double

    L2 regularization

27. val sparseOverhead: Double

    The cost model overhead for how much more expensive a sparse operation on dense data is compared to the respective dense operation

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

    Definition Classes

    AnyRef

29. def toString(): String

    Definition Classes

    AnyRef → Any

30. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

33. val weight: Int

    Definition Classes

    SparseLBFGSwithL2 → WeightedNode

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

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

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

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