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  • package org
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  • package apache
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  • 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.

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    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.

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    spark
    See also

    SPARK-4591 to track the progress of feature parity

  • package util
    Definition Classes
    mllib
  • DataValidators
  • KMeansDataGenerator
  • LinearDataGenerator
  • Loader
  • LogisticRegressionDataGenerator
  • MFDataGenerator
  • MLUtils
  • SVMDataGenerator
  • Saveable
o

org.apache.spark.mllib.util

LinearDataGenerator

object LinearDataGenerator

Generate sample data used for Linear Data. This class generates uniformly random values for every feature and adds Gaussian noise with mean eps to the response variable Y.

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LinearDataGenerator.scala
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  9. def generateLinearInput(intercept: Double, weights: Array[Double], xMean: Array[Double], xVariance: Array[Double], nPoints: Int, seed: Int, eps: Double, sparsity: Double): Seq[LabeledPoint]

    intercept

    Data intercept

    weights

    Weights to be applied.

    xMean

    the mean of the generated features. Lots of time, if the features are not properly standardized, the algorithm with poor implementation will have difficulty to converge.

    xVariance

    the variance of the generated features.

    nPoints

    Number of points in sample.

    seed

    Random seed

    eps

    Epsilon scaling factor.

    sparsity

    The ratio of zero elements. If it is 0.0, LabeledPoints with DenseVector is returned.

    returns

    Seq of input.

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    @Since( "1.6.0" )
  10. def generateLinearInput(intercept: Double, weights: Array[Double], xMean: Array[Double], xVariance: Array[Double], nPoints: Int, seed: Int, eps: Double): Seq[LabeledPoint]

    intercept

    Data intercept

    weights

    Weights to be applied.

    xMean

    the mean of the generated features. Lots of time, if the features are not properly standardized, the algorithm with poor implementation will have difficulty to converge.

    xVariance

    the variance of the generated features.

    nPoints

    Number of points in sample.

    seed

    Random seed

    eps

    Epsilon scaling factor.

    returns

    Seq of input.

    Annotations
    @Since( "0.8.0" )
  11. def generateLinearInput(intercept: Double, weights: Array[Double], nPoints: Int, seed: Int, eps: Double = 0.1): Seq[LabeledPoint]

    For compatibility, the generated data without specifying the mean and variance will have zero mean and variance of (1.0/3.0) since the original output range is [-1, 1] with uniform distribution, and the variance of uniform distribution is (b - a)2 / 12 which will be (1.0/3.0)

    For compatibility, the generated data without specifying the mean and variance will have zero mean and variance of (1.0/3.0) since the original output range is [-1, 1] with uniform distribution, and the variance of uniform distribution is (b - a)2 / 12 which will be (1.0/3.0)

    intercept

    Data intercept

    weights

    Weights to be applied.

    nPoints

    Number of points in sample.

    seed

    Random seed

    eps

    Epsilon scaling factor.

    returns

    Seq of input.

    Annotations
    @Since( "0.8.0" )
  12. def generateLinearInputAsList(intercept: Double, weights: Array[Double], nPoints: Int, seed: Int, eps: Double): List[LabeledPoint]

    Return a Java List of synthetic data randomly generated according to a multi collinear model.

    Return a Java List of synthetic data randomly generated according to a multi collinear model.

    intercept

    Data intercept

    weights

    Weights to be applied.

    nPoints

    Number of points in sample.

    seed

    Random seed

    returns

    Java List of input.

    Annotations
    @Since( "0.8.0" )
  13. def generateLinearRDD(sc: SparkContext, nexamples: Int, nfeatures: Int, eps: Double, nparts: Int = 2, intercept: Double = 0.0): RDD[LabeledPoint]

    Generate an RDD containing sample data for Linear Regression models - including Ridge, Lasso, and unregularized variants.

    Generate an RDD containing sample data for Linear Regression models - including Ridge, Lasso, and unregularized variants.

    sc

    SparkContext to be used for generating the RDD.

    nexamples

    Number of examples that will be contained in the RDD.

    nfeatures

    Number of features to generate for each example.

    eps

    Epsilon factor by which examples are scaled.

    nparts

    Number of partitions in the RDD. Default value is 2.

    returns

    RDD of LabeledPoint containing sample data.

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    @Since( "0.8.0" )
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