keystoneml.nodes.learning

BlockLinearMapper

class BlockLinearMapper extends Transformer[DenseVector[Double], DenseVector[Double]]

Transformer that applies a linear model to an input. Different from LinearMapper in that the matrix representing the transformation is split into a seq.

Linear Supertypes
Transformer[DenseVector[Double], DenseVector[Double]], Chainable[DenseVector[Double], DenseVector[Double]], TransformerOperator, Serializable, Serializable, Operator, AnyRef, Any
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  1. BlockLinearMapper
  2. Transformer
  3. Chainable
  4. TransformerOperator
  5. Serializable
  6. Serializable
  7. Operator
  8. AnyRef
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Instance Constructors

  1. new BlockLinearMapper(xs: Seq[DenseMatrix[Double]], blockSize: Int, bOpt: Option[DenseVector[Double]] = scala.None, featureScalersOpt: Option[Seq[Transformer[DenseVector[Double], DenseVector[Double]]]] = scala.None)

    xs

    The chunks of the matrix representing the linear model

    blockSize

    blockSize to split data before applying transformations

    bOpt

    optional intercept term to be added

    featureScalersOpt

    optional seq of transformers to be applied before transformation

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 andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: PipelineDataset[DenseVector[Double]], labels: PipelineDataset[L]): Pipeline[DenseVector[Double], C]

    Chains a label estimator onto the end of this pipeline, producing a new pipeline.

    Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    labels

    The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

    Definition Classes
    Chainable
  7. final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: RDD[DenseVector[Double]], labels: PipelineDataset[L]): Pipeline[DenseVector[Double], C]

    Chains a label estimator onto the end of this pipeline, producing a new pipeline.

    Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    labels

    The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

    Definition Classes
    Chainable
  8. final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: PipelineDataset[DenseVector[Double]], labels: RDD[L]): Pipeline[DenseVector[Double], C]

    Chains a label estimator onto the end of this pipeline, producing a new pipeline.

    Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    labels

    The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

    Definition Classes
    Chainable
  9. final def andThen[C, L](est: LabelEstimator[DenseVector[Double], C, L], data: RDD[DenseVector[Double]], labels: RDD[L]): Pipeline[DenseVector[Double], C]

    Chains a label estimator onto the end of this pipeline, producing a new pipeline.

    Chains a label estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    labels

    The labels to use when fitting the LabelEstimator. Must be zippable with the training data.

    Definition Classes
    Chainable
  10. final def andThen[C](est: Estimator[DenseVector[Double], C], data: PipelineDataset[DenseVector[Double]]): Pipeline[DenseVector[Double], C]

    Chains an estimator onto the end of this pipeline, producing a new pipeline.

    Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    Definition Classes
    Chainable
  11. final def andThen[C](est: Estimator[DenseVector[Double], C], data: RDD[DenseVector[Double]]): Pipeline[DenseVector[Double], C]

    Chains an estimator onto the end of this pipeline, producing a new pipeline.

    Chains an estimator onto the end of this pipeline, producing a new pipeline. If this pipeline has already been executed, it will not need to be fit again.

    est

    The estimator to chain onto the end of this pipeline

    data

    The training data to use (the estimator will be fit on the result of passing this data through the current pipeline)

    Definition Classes
    Chainable
  12. final def andThen[C](next: Chainable[DenseVector[Double], C]): Pipeline[DenseVector[Double], C]

    Chains a pipeline onto the end of this one, producing a new pipeline.

    Chains a pipeline onto the end of this one, producing a new pipeline. If either this pipeline or the following has already been executed, it will not need to be fit again.

    next

    the pipeline to chain

    Definition Classes
    Chainable
  13. def apply(in: DenseVector[Double]): DenseVector[Double]

    The application of this Transformer to a single input item.

    The application of this Transformer to a single input item. This method MUST be overridden by ML developers.

    in

    The input item to pass into this transformer

    returns

    The output value

    Definition Classes
    BlockLinearMapperTransformer
  14. def apply(in: Seq[RDD[DenseVector[Double]]]): RDD[DenseVector[Double]]

    Applies the linear model to feature vectors large enough to have been split into several RDDs.

    Applies the linear model to feature vectors large enough to have been split into several RDDs.

    returns

    the output vectors

  15. def apply(in: RDD[DenseVector[Double]]): RDD[DenseVector[Double]]

    Applies the linear model to feature vectors large enough to have been split into several RDDs.

    Applies the linear model to feature vectors large enough to have been split into several RDDs.

    in

    RDD of vectors to apply the model to

    returns

    the output vectors

    Definition Classes
    BlockLinearMapperTransformer
  16. def applyAndEvaluate(in: Seq[RDD[DenseVector[Double]]], evaluator: (RDD[DenseVector[Double]]) ⇒ Unit): Unit

    Applies the linear model to feature vectors.

    Applies the linear model to feature vectors. After processing chunk i of every vector, applies

    in

    sequence of input RDD chunks

    evaluator

    to the intermediate output vector.

  17. def applyAndEvaluate(in: RDD[DenseVector[Double]], evaluator: (RDD[DenseVector[Double]]) ⇒ Unit): Unit

    Applies the linear model to feature vectors.

    Applies the linear model to feature vectors. After processing chunk i of every vector, applies

    in

    input RDD

    evaluator

    to the intermediate output vector.

  18. final def asInstanceOf[T0]: T0

    Definition Classes
    Any
  19. val bOpt: Option[DenseVector[Double]]

    optional intercept term to be added

  20. val blockSize: Int

    blockSize to split data before applying transformations

  21. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  22. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  23. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  24. def execute(deps: Seq[Expression]): Expression

    Definition Classes
    TransformerOperator → Operator
  25. val featureScalers: Seq[Transformer[DenseVector[Double], DenseVector[Double]]]

  26. val featureScalersOpt: Option[Seq[Transformer[DenseVector[Double], DenseVector[Double]]]]

    optional seq of transformers to be applied before transformation

  27. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  28. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any
  29. def hashCode(): Int

    Definition Classes
    AnyRef → Any
  30. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any
  31. def label: String

    Definition Classes
    Operator
  32. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  33. final def notify(): Unit

    Definition Classes
    AnyRef
  34. final def notifyAll(): Unit

    Definition Classes
    AnyRef
  35. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef
  36. def toPipeline: Pipeline[DenseVector[Double], DenseVector[Double]]

    A method that converts this object into a Pipeline.

    A method that converts this object into a Pipeline. Must be implemented by anything that extends Chainable.

    Definition Classes
    TransformerChainable
  37. def toString(): String

    Definition Classes
    AnyRef → Any
  38. val vectorSplitter: VectorSplitter

  39. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  40. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  41. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  42. val xs: Seq[DenseMatrix[Double]]

    The chunks of the matrix representing the linear model

Inherited from Transformer[DenseVector[Double], DenseVector[Double]]

Inherited from Chainable[DenseVector[Double], DenseVector[Double]]

Inherited from TransformerOperator

Inherited from Serializable

Inherited from Serializable

Inherited from Operator

Inherited from AnyRef

Inherited from Any

Ungrouped