org.apache.spark.mllib.optimization

GradientDescent

object GradientDescent extends Logging

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  30. def runMiniBatchSGD(data: RDD[(Double, Array[Double])], gradient: Gradient, updater: Updater, stepSize: Double, numIterations: Int, regParam: Double, miniBatchFraction: Double, initialWeights: Array[Double]): (Array[Double], Array[Double])

    Run gradient descent in parallel using mini batches.

    Run gradient descent in parallel using mini batches.

    data

    - Input data for SGD. RDD of form (label, [feature values]).

    gradient

    - Gradient object that will be used to compute the gradient.

    updater

    - Updater object that will be used to update the model.

    stepSize

    - stepSize to be used during update.

    numIterations

    - number of iterations that SGD should be run.

    regParam

    - regularization parameter

    miniBatchFraction

    - fraction of the input data set that should be used for one iteration of SGD. Default value 1.0.

    returns

    A tuple containing two elements. The first element is a column matrix containing weights for every feature, and the second element is an array containing the stochastic loss computed for every iteration.

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