nodes.learning

BlockLeastSquaresEstimator

class BlockLeastSquaresEstimator extends LabelEstimator[DenseVector[Double], DenseVector[Double], DenseVector[Double]]

Fits a least squares model using block coordinate descent with provided training features and labels

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LabelEstimator[DenseVector[Double], DenseVector[Double], DenseVector[Double]], EstimatorNode, Serializable, Serializable, Node, AnyRef, Any
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  1. BlockLeastSquaresEstimator
  2. LabelEstimator
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Instance Constructors

  1. new BlockLeastSquaresEstimator(blockSize: Int, numIter: Int, lambda: Double = 0.0, numFeaturesOpt: Option[Int] = scala.None)

    blockSize

    size of block to use in the solver

    numIter

    number of iterations of solver to run

    lambda

    L2-regularization to use

Value Members

  1. final def !=(arg0: AnyRef): Boolean

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  10. def finalize(): Unit

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  11. def fit(trainingFeatures: RDD[DenseVector[Double]], trainingLabels: RDD[DenseVector[Double]], numFeaturesOpt: Option[Int]): BlockLinearMapper

  12. def fit(trainingFeatures: RDD[DenseVector[Double]], trainingLabels: RDD[DenseVector[Double]]): BlockLinearMapper

    Fit a model after splitting training data into appropriate blocks.

    Fit a model after splitting training data into appropriate blocks.

    trainingFeatures

    training data to use in one RDD.

    trainingLabels

    labels for training data in a RDD.

    returns

    A Transformer which can be called on new data.

    Definition Classes
    BlockLeastSquaresEstimator → LabelEstimator

  13. def fit(trainingFeatures: Seq[RDD[DenseVector[Double]]], trainingLabels: RDD[DenseVector[Double]]): BlockLinearMapper

    Fit a model using blocks of features and labels provided.

    Fit a model using blocks of features and labels provided.

    trainingFeatures

    feature blocks to use in RDDs.

    trainingLabels

    RDD of labels to use.

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

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  26. def withData(data: RDD[DenseVector[Double]], labels: RDD[DenseVector[Double]]): Pipeline[DenseVector[Double], DenseVector[Double]]

    Constructs a pipeline from a single label estimator and training data.

    Constructs a pipeline from a single label estimator and training data. Equivalent to Pipeline() andThen (estimator, data, labels)

    data

    The training data

    labels

    The training labels

    Definition Classes
    LabelEstimator

Inherited from LabelEstimator[DenseVector[Double], DenseVector[Double], DenseVector[Double]]

Inherited from EstimatorNode

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