keystoneml.nodes.learning

DistributedColumnPCAEstimator

case class DistributedColumnPCAEstimator(dims: Int) extends Estimator[DenseMatrix[Float], DenseMatrix[Float]] with CostModel with Product with Serializable

Estimates a PCA model for dimensionality reduction based on a sample of a larger input dataset, using a distributed PCA algorithm. Treats each column of the input matrices like a separate DenseVector input to DistributedPCAEstimator.

dims

Dimensions to reduce input dataset to.

Linear Supertypes
Product, Equals, CostModel, Estimator[DenseMatrix[Float], DenseMatrix[Float]], EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
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Inherited
  1. DistributedColumnPCAEstimator
  2. Product
  3. Equals
  4. CostModel
  5. Estimator
  6. EstimatorOperator
  7. Serializable
  8. Serializable
  9. Operator
  10. AnyRef
  11. Any
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Instance Constructors

  1. new DistributedColumnPCAEstimator(dims: Int)

    dims

    Dimensions to reduce input dataset to.

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
    DistributedColumnPCAEstimator → CostModel

  9. val dims: Int

    Dimensions to reduce input dataset to.

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

    Definition Classes
    AnyRef

  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(data: RDD[DenseMatrix[Float]]): Transformer[DenseMatrix[Float], DenseMatrix[Float]]

    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.

    returns

    A new transformer

    Definition Classes
    DistributedColumnPCAEstimator → Estimator

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

    Definition Classes
    AnyRef → Any

  15. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  16. def label: String

    Definition Classes
    Operator

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

    Definition Classes
    AnyRef

  18. final def notify(): Unit

    Definition Classes
    AnyRef

  19. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  20. val pcaEstimator: DistributedPCAEstimator

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

    Definition Classes
    AnyRef

  22. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  25. final def withData(data: PipelineDataset[DenseMatrix[Float]]): Pipeline[DenseMatrix[Float], DenseMatrix[Float]]

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

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

    data

    The training data

    returns

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

    Definition Classes
    Estimator

  26. final def withData(data: RDD[DenseMatrix[Float]]): Pipeline[DenseMatrix[Float], DenseMatrix[Float]]

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

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

    data

    The training data

    returns

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

    Definition Classes
    Estimator

Inherited from Product

Inherited from Equals

Inherited from CostModel

Inherited from Estimator[DenseMatrix[Float], DenseMatrix[Float]]

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

Ungrouped