keystoneml.nodes.learning

BlockLeastSquaresEstimator

class BlockLeastSquaresEstimator extends LabelEstimator[DenseVector[Double], DenseVector[Double], DenseVector[Double]] with WeightedNode with CostModel

Fits a least squares model using block coordinate descent with provided training features and labels

Linear Supertypes
CostModel, WeightedNode, LabelEstimator[DenseVector[Double], DenseVector[Double], DenseVector[Double]], EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
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Inherited
  1. BlockLeastSquaresEstimator
  2. CostModel
  3. WeightedNode
  4. LabelEstimator
  5. EstimatorOperator
  6. Serializable
  7. Serializable
  8. Operator
  9. AnyRef
  10. Any
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Instance Constructors

  1. new BlockLeastSquaresEstimator(blockSize: Int, numIter: Int, lambda: Double = 0.0, numFeaturesOpt: Option[Int] = scala.None)

    blockSize

    size of block to use in the solver

    numIter

    number of iterations of solver to run

    lambda

    L2-regularization to use

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

    Definition Classes
    BlockLeastSquaresEstimator → 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]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  13. def fit(trainingFeatures: RDD[DenseVector[Double]], trainingLabels: RDD[DenseVector[Double]], numFeaturesOpt: Option[Int]): BlockLinearMapper

  14. def fit(trainingFeatures: RDD[DenseVector[Double]], trainingLabels: RDD[DenseVector[Double]]): BlockLinearMapper

    Fit a model after splitting training data into appropriate blocks.

    Fit a model after splitting training data into appropriate blocks.

    trainingFeatures

    training data to use in one RDD.

    trainingLabels

    labels for training data in a RDD.

    returns

    A new transformer

    Definition Classes
    BlockLeastSquaresEstimator → LabelEstimator

  15. def fit(trainingFeatures: Seq[RDD[DenseVector[Double]]], trainingLabels: RDD[DenseVector[Double]]): BlockLinearMapper

    Fit a model using blocks of features and labels provided.

    Fit a model using blocks of features and labels provided.

    trainingFeatures

    feature blocks to use in RDDs.

    trainingLabels

    RDD of labels to use.

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

    Definition Classes
    AnyRef → Any

  17. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  18. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  19. def label: String

    Definition Classes
    Operator

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

    Definition Classes
    AnyRef

  21. final def notify(): Unit

    Definition Classes
    AnyRef

  22. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  24. def toString(): String

    Definition Classes
    AnyRef → Any

  25. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  28. val weight: Int

    Definition Classes
    BlockLeastSquaresEstimator → WeightedNode

  29. 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

  30. 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

  31. 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

  32. 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

Inherited from CostModel

Inherited from WeightedNode

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

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

Ungrouped