keystoneml.nodes.learning

ApproximatePCAEstimator

class ApproximatePCAEstimator extends Estimator[DenseVector[Float], DenseVector[Float]] with Logging

Approximately estimates a PCA model for dimensionality reduction based on an input dataset.

Linear Supertypes
Logging, Estimator[DenseVector[Float], DenseVector[Float]], EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
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  1. ApproximatePCAEstimator
  2. Logging
  3. Estimator
  4. EstimatorOperator
  5. Serializable
  6. Serializable
  7. Operator
  8. AnyRef
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Instance Constructors

  1. new ApproximatePCAEstimator(dims: Int, q: Int = 10, p: Int = 5)

    dims

    Dimensions to reduce input dataset to.

    q

    The number of iterations to use

    p

    The amount of padding to add beyond dims for the calculations

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. def approximatePCA(data: DenseMatrix[Float], k: Int, q: Int = 10, p: Int = 5): DenseMatrix[Float]

  7. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  8. def clone(): AnyRef

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

  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(samples: RDD[DenseVector[Float]]): PCATransformer

    Computes the PCA using a sketch-based algorithm.

    Computes the PCA using a sketch-based algorithm.

    samples

    A sample of features to be reduced. Often O(1e6). Logically row-major.

    returns

    A PCA Matrix which will perform dimensionality reduction when applied to a data matrix.

    Definition Classes
    ApproximatePCAEstimator → Estimator

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

    Definition Classes
    AnyRef → Any

  15. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  16. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  17. def label: String

    Definition Classes
    Operator

  18. def log: Logger

    Attributes
    protected
    Definition Classes
    Logging

  19. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  20. def logDebug(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  21. def logError(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  22. def logError(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  23. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  24. def logInfo(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  25. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  26. def logTrace(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

  27. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes
    protected
    Definition Classes
    Logging

  28. def logWarning(msg: ⇒ String): Unit

    Attributes
    protected
    Definition Classes
    Logging

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

    Definition Classes
    AnyRef

  30. final def notify(): Unit

    Definition Classes
    AnyRef

  31. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  33. def toString(): String

    Definition Classes
    AnyRef → Any

  34. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  37. final def withData(data: PipelineDataset[DenseVector[Float]]): Pipeline[DenseVector[Float], DenseVector[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

  38. final def withData(data: RDD[DenseVector[Float]]): Pipeline[DenseVector[Float], DenseVector[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 Logging

Inherited from Estimator[DenseVector[Float], DenseVector[Float]]

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

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