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class GradientDescent extends Optimizer with Logging

Class used to solve an optimization problem using Gradient Descent.

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  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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  14. def isTraceEnabled(): Boolean
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  15. def log: Logger
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  16. def logDebug(msg: => String, throwable: Throwable): Unit
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  17. def logDebug(msg: => String): Unit
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  18. def logError(msg: => String, throwable: Throwable): Unit
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  19. def logError(msg: => String): Unit
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  20. def logInfo(msg: => String, throwable: Throwable): Unit
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  21. def logInfo(msg: => String): Unit
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  22. def logName: String
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  23. def logTrace(msg: => String, throwable: Throwable): Unit
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  24. def logTrace(msg: => String): Unit
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  25. def logWarning(msg: => String, throwable: Throwable): Unit
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  26. def logWarning(msg: => String): Unit
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  29. final def notifyAll(): Unit
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  30. def optimize(data: RDD[(Double, Vector)], initialWeights: Vector): Vector

    Runs gradient descent on the given training data.

    Runs gradient descent on the given training data.

    data

    training data

    initialWeights

    initial weights

    returns

    solution vector

    Definition Classes
    GradientDescent → Optimizer
  31. def optimizeWithLossReturned(data: RDD[(Double, Vector)], initialWeights: Vector): (Vector, Array[Double])

    Runs gradient descent on the given training data.

    Runs gradient descent on the given training data.

    data

    training data

    initialWeights

    initial weights

    returns

    solution vector and loss value in an array

  32. def setConvergenceTol(tolerance: Double): GradientDescent.this.type

    Set the convergence tolerance.

    Set the convergence tolerance. Default 0.001 convergenceTol is a condition which decides iteration termination. The end of iteration is decided based on below logic.

    • If the norm of the new solution vector is greater than 1, the diff of solution vectors is compared to relative tolerance which means normalizing by the norm of the new solution vector.
    • If the norm of the new solution vector is less than or equal to 1, the diff of solution vectors is compared to absolute tolerance which is not normalizing.

    Must be between 0.0 and 1.0 inclusively.

  33. 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.

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

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

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

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

    Set the number of iterations for SGD.

    Set the number of iterations for SGD. Default 100.

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

    Set the regularization parameter.

    Set the regularization parameter. Default 0.0.

  37. 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)

  38. 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.

  39. final def synchronized[T0](arg0: => T0): T0
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  40. def toString(): String
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  41. final def wait(): Unit
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  42. final def wait(arg0: Long, arg1: Int): Unit
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  43. final def wait(arg0: Long): Unit
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Inherited from Logging

Inherited from Optimizer

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