keystoneml.nodes.learning

ZCAWhitenerEstimator

class ZCAWhitenerEstimator extends Estimator[DenseMatrix[Double], DenseMatrix[Double]]

Computes a ZCA Whitener, which is intended to rotate an input dataset to identity covariance. The "Z" in ZCA Whitening means that the solution will be as close to the original dataset as possible while having this identity covariance property.

See here for more details: http://ufldl.stanford.edu/wiki/index.php/Whitening

Linear Supertypes
Estimator[DenseMatrix[Double], DenseMatrix[Double]], EstimatorOperator, Serializable, Serializable, Operator, AnyRef, Any
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Inherited
  1. ZCAWhitenerEstimator
  2. Estimator
  3. EstimatorOperator
  4. Serializable
  5. Serializable
  6. Operator
  7. AnyRef
  8. Any
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Instance Constructors

  1. new ZCAWhitenerEstimator(eps: Double = 0.1)

    eps

    Regularization Parameter

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 eps: Double

    Regularization Parameter

  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(in: RDD[DenseMatrix[Double]]): ZCAWhitener

    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.

    returns

    A new transformer

    Definition Classes
    ZCAWhitenerEstimator → Estimator

  14. def fitSingle(in: DenseMatrix[Double]): ZCAWhitener

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

    Definition Classes
    AnyRef → Any

  16. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  17. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  18. def label: String

    Definition Classes
    Operator

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

    Definition Classes
    AnyRef

  20. final def notify(): Unit

    Definition Classes
    AnyRef

  21. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  23. def toString(): String

    Definition Classes
    AnyRef → Any

  24. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  27. final def withData(data: PipelineDataset[DenseMatrix[Double]]): Pipeline[DenseMatrix[Double], DenseMatrix[Double]]

    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

  28. final def withData(data: RDD[DenseMatrix[Double]]): Pipeline[DenseMatrix[Double], DenseMatrix[Double]]

    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 Estimator[DenseMatrix[Double], DenseMatrix[Double]]

Inherited from EstimatorOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

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