util

Type Members

1. case class AllSparseFeatures[T]()(implicit evidence$1: ClassTag[T]) extends Estimator[Seq[(T, Double)], SparseVector[Double]] with Product with Serializable

   An Estimator that chooses all sparse features observed when training, and produces a transformer which builds a sparse vector out of them.

2. case class Cacher[T](name: Option[String] = scala.None)(implicit evidence$1: ClassTag[T]) extends Transformer[T, T] with Logging with Product with Serializable

   Caches an RDD at a given point within a Pipeline.

3. case class ClassLabelIndicatorsFromIntArrayLabels(numClasses: Int, validate: Boolean = false) extends Transformer[Array[Int], DenseVector[Double]] with Product with Serializable

   Given a set of class labels, returns a binary vector that indicates when each class is present.

4. case class ClassLabelIndicatorsFromIntLabels(numClasses: Int) extends Transformer[Int, DenseVector[Double]] with Product with Serializable

   Given a class label, returns a binary vector that indicates when that class is present.

5. case class CommonSparseFeatures[T](numFeatures: Int)(implicit evidence$1: ClassTag[T]) extends Estimator[Seq[(T, Double)], SparseVector[Double]] with Product with Serializable

   An Estimator that chooses the most frequently observed sparse features when training, and produces a transformer which builds a sparse vector out of them

6. case class Densify[T <: Vector[Double]]() extends Transformer[T, DenseVector[Double]] with Product with Serializable

   Transformer to densify vectors into DenseVectors.

7. class Identity[T] extends Transformer[T, T]

   This class performs a no-op on its input.

8. class Shuffler[T] extends Transformer[T, T] with Logging

   Randomly shuffle the rows of an RDD within a pipeline.

9. class SparseFeatureVectorizer[T] extends Transformer[Seq[(T, Double)], SparseVector[Double]]

   A transformer which given a feature space, maps features of the form (feature id, value) into a sparse vector

10. case class Sparsify[T <: Vector[Double]]() extends Transformer[T, SparseVector[Double]] with Product with Serializable

    Transformer to convert vectors into SparseVectors.

11. class TopKClassifier extends Transformer[DenseVector[Double], Array[Int]]

    Transformer that returns the indices of the largest k values of the vector, in order

12. case class VectorCombiner[T]()(implicit evidence$1: ClassTag[T], zero: Zero[T]) extends Transformer[Seq[DenseVector[T]], DenseVector[T]] with Product with Serializable

    Concats a Seq of DenseVectors into a single DenseVector.

13. class VectorSplitter extends FunctionNode[RDD[DenseVector[Double]], Seq[RDD[DenseVector[Double]]]]

    This transformer splits the input vector into a number of blocks.