Class/Object

keystoneml.nodes.learning

PerClassWeightedLeastSquaresEstimator

Related Docs: object PerClassWeightedLeastSquaresEstimator | package learning

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

Train a weighted block-coordinate descent model using least squares

Linear Supertypes
LabelEstimator[DenseVector[Double], DenseVector[Double], DenseVector[Double]], EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
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Inherited
  1. PerClassWeightedLeastSquaresEstimator
  2. LabelEstimator
  3. EstimatorOperator
  4. Serializable
  5. Serializable
  6. Operator
  7. AnyRef
  8. Any
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Visibility
  1. Public
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Instance Constructors

  1. new PerClassWeightedLeastSquaresEstimator(blockSize: Int, numIter: Int, lambda: Double, mixtureWeight: Double, numFeaturesOpt: Option[Int] = None)

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    blockSize

    size of blocks to use

    numIter

    number of passes of co-ordinate descent to run

    lambda

    regularization parameter

    mixtureWeight

    how much should positive samples be weighted

Value Members

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

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    AnyRef → Any

  2. final def ##(): Int

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    AnyRef → Any

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

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    Definition Classes
    AnyRef → Any

  4. final def asInstanceOf[T0]: T0

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    Any

  5. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    AnyRef

  7. def equals(arg0: Any): Boolean

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    AnyRef → Any

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

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    Definition Classes
    EstimatorOperator → Operator

  9. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

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    Fit a weighted least squares model using blocks of features provided.

    Fit a weighted least squares model using blocks of features provided.

    NOTE: This function makes multiple passes over the training data. Caching

    trainingFeatures

    Blocks of training data RDDs

    trainingLabels

    training labels RDD

    returns

    A new transformer@returns A BlockLinearMapper that contains the model, intercept

    Definition Classes
    PerClassWeightedLeastSquaresEstimator → LabelEstimator

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

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  12. def hashCode(): Int

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  13. final def isInstanceOf[T0]: Boolean

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    Any

  14. def label: String

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    Definition Classes
    Operator

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

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  16. final def notify(): Unit

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    AnyRef

  17. final def notifyAll(): Unit

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    AnyRef

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

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  19. def toString(): String

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  20. final def wait(): Unit

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    @throws( ... )

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

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    @throws( ... )

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

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

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    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

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

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    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

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

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    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

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

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    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 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

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