GradientDescent

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

   

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2. final def ##(): Int

   

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8. def finalize(): Unit

   

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

   

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10. def hashCode(): Int

    

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11. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    

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13. def isTraceEnabled(): Boolean

    

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14. def log: Logger

    

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15. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    

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16. def logDebug(msg: ⇒ String): Unit

    

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17. def logError(msg: ⇒ String, throwable: Throwable): Unit

    

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18. def logError(msg: ⇒ String): Unit

    

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19. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    

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20. def logInfo(msg: ⇒ String): Unit

    

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21. def logName: String

    

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22. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    

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23. def logTrace(msg: ⇒ String): Unit

    

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24. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    

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25. def logWarning(msg: ⇒ String): Unit

    

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26. final def ne(arg0: AnyRef): Boolean

    

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27. final def notify(): Unit

    

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28. final def notifyAll(): Unit

    

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

    

    Alias of runMiniBatchSGD with convergenceTol set to default value of 0.001.

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

    convergenceTol

    Minibatch iteration will end before numIterations if the relative difference between the current weight and the previous weight is less than this value. In measuring convergence, L2 norm is calculated. Default value 0.001. Must be between 0.0 and 1.0 inclusively.

    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.

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

    

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32. def toString(): String

    

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33. final def wait(): Unit

    

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34. final def wait(arg0: Long, arg1: Int): Unit

    

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

    

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