keystoneml.nodes.learning

KernelRidgeRegression

object KernelRidgeRegression extends Logging with Serializable

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  30. def trainWithL2[T](trainKernelMat: KernelMatrix, labels: RDD[DenseVector[Double]], lambda: Double, blockSize: Int, numEpochs: Int, blockPermuter: Option[Long], blocksBeforeCheckpoint: Int = 25)(implicit arg0: ClassTag[T]): Seq[DenseMatrix[Double]]

    Solves a linear system of the form (K + \lambda * I) * W = Y using Gauss-Seidel based Block Coordinate Descent as described in http://arxiv.

    Solves a linear system of the form (K + \lambda * I) * W = Y using Gauss-Seidel based Block Coordinate Descent as described in http://arxiv.org/abs/1602.05310

    K is assumed to be a symmetric kernel matrix generated using a kernel generator.

    labels

    training labels RDD

    lambda

    L2 regularization parameter

    blockSize

    number of columns per block of Gauss-Seidel solve

    numEpochs

    number of passes of co-ordinate descent to run

    blockPermuter

    seed to use for permuting column blocks

    blocksBeforeCheckpoint

    frequency at which intermediate data should be checkpointed

    returns

    a model that can be applied on test data.

  31. def updateModel(model: RDD[IndexedSeq[DenseMatrix[Double]]], wBlockNewBC: Seq[Broadcast[DenseMatrix[Double]]], blockIdxsBC: Broadcast[Array[Int]], preFixLengthsBC: Broadcast[Array[Int]]): RDD[IndexedSeq[DenseMatrix[Double]]]

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