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

Top-level method to run gradient descent.

Source
GradientDescent.scala
Linear Supertypes
Serializable, Logging, AnyRef, Any
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  1. GradientDescent
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Type Members

  1. implicit class LogStringContext extends AnyRef
    Definition Classes
    Logging

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def MDC(key: LogKey, value: Any): MDC
    Attributes
    protected
    Definition Classes
    Logging
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  9. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  10. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  11. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  12. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  13. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  14. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  15. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  16. def logBasedOnLevel(level: Level)(f: => MessageWithContext): Unit
    Attributes
    protected
    Definition Classes
    Logging
  17. def logDebug(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  18. def logDebug(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  19. def logDebug(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  20. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  21. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  22. def logError(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  23. def logError(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  24. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  25. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  26. def logInfo(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  27. def logInfo(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  28. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  29. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  30. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  31. def logTrace(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  32. def logTrace(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  33. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  34. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  35. def logWarning(entry: LogEntry, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  36. def logWarning(entry: LogEntry): Unit
    Attributes
    protected
    Definition Classes
    Logging
  37. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  38. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  39. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  40. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  41. 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.

  42. 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.

  43. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  44. def toString(): String
    Definition Classes
    AnyRef → Any
  45. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  46. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  47. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  48. def withLogContext(context: Map[String, String])(body: => Unit): Unit
    Attributes
    protected
    Definition Classes
    Logging

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

Inherited from Serializable

Inherited from Logging

Inherited from AnyRef

Inherited from Any

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