GradientDescent

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

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30. def optimize(data: RDD[(Double, Vector)], initialWeights: Vector): Vector

    :: DeveloperApi :: Runs gradient descent on the given training data.

    :: DeveloperApi :: Runs gradient descent on the given training data.

    data

    training data

    initialWeights

    initial weights

    returns

    solution vector

    Definition Classes

    GradientDescent → Optimizer

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    @DeveloperApi()

31. def setGradient(gradient: Gradient): GradientDescent.this.type

    Set the gradient function (of the loss function of one single data example) to be used for SGD.

32. def setMiniBatchFraction(fraction: Double): GradientDescent.this.type

    :: Experimental :: Set fraction of data to be used for each SGD iteration.

    :: Experimental :: Set fraction of data to be used for each SGD iteration. Default 1.0 (corresponding to deterministic/classical gradient descent)

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    @Experimental()

33. def setNumIterations(iters: Int): GradientDescent.this.type

    Set the number of iterations for SGD.

    Set the number of iterations for SGD. Default 100.

34. def setRegParam(regParam: Double): GradientDescent.this.type

    Set the regularization parameter.

    Set the regularization parameter. Default 0.0.

35. def setStepSize(step: Double): GradientDescent.this.type

    Set the initial step size of SGD for the first step.

    Set the initial step size of SGD for the first step. Default 1.0. In subsequent steps, the step size will decrease with stepSize/sqrt(t)

36. def setUpdater(updater: Updater): GradientDescent.this.type

    Set the updater function to actually perform a gradient step in a given direction.

    Set the updater function to actually perform a gradient step in a given direction. The updater is responsible to perform the update from the regularization term as well, and therefore determines what kind or regularization is used, if any.

37. final def synchronized[T0](arg0: ⇒ T0): T0

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41. final def wait(arg0: Long): Unit

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