BlockWeightedLeastSquaresEstimator

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6. def addPairMatrices(a: (DenseMatrix[Double], DenseMatrix[Double]), b: (DenseMatrix[Double], DenseMatrix[Double])): (DenseMatrix[Double], DenseMatrix[Double])

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

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13. def groupByClasses(features: Seq[RDD[DenseVector[Double]]], labels: RDD[DenseVector[Double]]): (Seq[RDD[DenseVector[Double]]], RDD[DenseVector[Double]])

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32. def trainWithL2(trainingFeatures: Seq[RDD[DenseVector[Double]]], trainingLabels: RDD[DenseVector[Double]], blockSize: Int, numIter: Int, lambda: Double, mixtureWeight: Double): BlockLinearMapper

    Returns a weighted block-coordinate descent model using least squares NOTE: This function assumes that the trainingFeatures have been partitioned by their class index.

    Returns a weighted block-coordinate descent model using least squares NOTE: This function assumes that the trainingFeatures have been partitioned by their class index. i.e. each partition of training data contains data for a single class

    NOTE: This function makes multiple passes over the training data. Caching @trainingFeatures and @trainingLabels before calling this function is recommended.

    trainingFeatures

    Blocks of training data RDDs

    trainingLabels

    training labels RDD

    numIter

    number of passes of co-ordinate descent to run

    lambda

    regularization parameter

    mixtureWeight

    how much should positive samples be weighted

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