org.apache.spark.mllib.optimization

GradientDescent

object GradientDescent extends Logging with Serializable

:: DeveloperApi :: Top-level method to run gradient descent.

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

    Run stochastic gradient descent (SGD) in parallel using mini batches.

    Run stochastic gradient descent (SGD) in parallel using mini batches. In each iteration, we sample a subset (fraction miniBatchFraction) of the total data in order to compute a gradient estimate. Sampling, and averaging the subgradients over this subset is performed using one standard spark map-reduce in each iteration.

    data

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

    gradient

    - Gradient object (used to compute the gradient of the loss function of one single data example)

    updater

    - Updater function to actually perform a gradient step in a given direction.

    stepSize

    - initial step size for the first step

    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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