Packages

  • package root
    Definition Classes
    root
  • package org
    Definition Classes
    root
  • package apache
    Definition Classes
    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

    Definition Classes
    apache
  • package mllib

    RDD-based machine learning APIs (in maintenance mode).

    RDD-based machine learning APIs (in maintenance mode).

    The spark.mllib package is in maintenance mode as of the Spark 2.0.0 release to encourage migration to the DataFrame-based APIs under the org.apache.spark.ml package. While in maintenance mode,

    • no new features in the RDD-based spark.mllib package will be accepted, unless they block implementing new features in the DataFrame-based spark.ml package;
    • bug fixes in the RDD-based APIs will still be accepted.

    The developers will continue adding more features to the DataFrame-based APIs in the 2.x series to reach feature parity with the RDD-based APIs. And once we reach feature parity, this package will be deprecated.

    Definition Classes
    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package optimization
    Definition Classes
    mllib
  • Gradient
  • GradientDescent
  • HingeGradient
  • L1Updater
  • LBFGS
  • LeastSquaresGradient
  • LogisticGradient
  • Optimizer
  • SimpleUpdater
  • SquaredL2Updater
  • Updater

object GradientDescent extends Logging with Serializable

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

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@DeveloperApi()
Linear Supertypes
Serializable, Serializable, Logging, AnyRef, Any
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  1. final def !=(arg0: Any): Boolean
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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    protected[java.lang]
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    @native() @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: Any): Boolean
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  8. def finalize(): Unit
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  9. final def getClass(): Class[_]
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    @native()
  10. def hashCode(): Int
    Definition Classes
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    @native()
  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
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    Logging
  13. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  14. def isTraceEnabled(): Boolean
    Attributes
    protected
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    Logging
  15. def log: Logger
    Attributes
    protected
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    Logging
  16. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  17. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  18. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  19. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  20. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  21. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  22. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  23. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  24. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  25. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
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    Logging
  26. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
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    Logging
  27. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  28. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  29. final def notifyAll(): Unit
    Definition Classes
    AnyRef
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    @native()
  30. 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.

  31. 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.

  32. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  33. def toString(): String
    Definition Classes
    AnyRef → Any
  34. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  35. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
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    @throws( ... )
  36. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
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    @native() @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

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