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

class L1Updater extends Updater

Updater for L1 regularized problems. R(w) = ||w||_1 Uses a step-size decreasing with the square root of the number of iterations.

Instead of subgradient of the regularizer, the proximal operator for the L1 regularization is applied after the gradient step. This is known to result in better sparsity of the intermediate solution.

The corresponding proximal operator for the L1 norm is the soft-thresholding function. That is, each weight component is shrunk towards 0 by shrinkageVal.

If w is greater than shrinkageVal, set weight component to w-shrinkageVal. If w is less than -shrinkageVal, set weight component to w+shrinkageVal. If w is (-shrinkageVal, shrinkageVal), set weight component to 0.

Equivalently, set weight component to signum(w) * max(0.0, abs(w) - shrinkageVal)

Source
Updater.scala
Linear Supertypes
Updater, Serializable, Serializable, AnyRef, Any
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  1. L1Updater
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Instance Constructors

  1. new L1Updater()

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  1. final def !=(arg0: Any): Boolean
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  5. def clone(): AnyRef
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    @throws( ... ) @native() @IntrinsicCandidate()
  6. def compute(weightsOld: Vector, gradient: Vector, stepSize: Double, iter: Int, regParam: Double): (Vector, Double)

    Compute an updated value for weights given the gradient, stepSize, iteration number and regularization parameter.

    Compute an updated value for weights given the gradient, stepSize, iteration number and regularization parameter. Also returns the regularization value regParam * R(w) computed using the *updated* weights.

    weightsOld

    - Column matrix of size dx1 where d is the number of features.

    gradient

    - Column matrix of size dx1 where d is the number of features.

    stepSize

    - step size across iterations

    iter

    - Iteration number

    regParam

    - Regularization parameter

    returns

    A tuple of 2 elements. The first element is a column matrix containing updated weights, and the second element is the regularization value computed using updated weights.

    Definition Classes
    L1Updater → Updater
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: Any): Boolean
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    AnyRef → Any
  9. final def getClass(): Class[_]
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    @native() @IntrinsicCandidate()
  10. def hashCode(): Int
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  11. final def isInstanceOf[T0]: Boolean
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  12. final def ne(arg0: AnyRef): Boolean
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  13. final def notify(): Unit
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    @native() @IntrinsicCandidate()
  14. final def notifyAll(): Unit
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    @native() @IntrinsicCandidate()
  15. final def synchronized[T0](arg0: ⇒ T0): T0
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  16. def toString(): String
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  17. final def wait(arg0: Long, arg1: Int): Unit
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Deprecated Value Members

  1. def finalize(): Unit
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    Deprecated

Inherited from Updater

Inherited from Serializable

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