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 feature
    Definition Classes
    mllib
  • ChiSqSelector
  • ChiSqSelectorModel
  • ElementwiseProduct
  • HashingTF
  • IDF
  • IDFModel
  • Normalizer
  • PCA
  • PCAModel
  • StandardScaler
  • StandardScalerModel
  • VectorTransformer
  • Word2Vec
  • Word2VecModel

class StandardScaler extends Logging

Standardizes features by removing the mean and scaling to unit std using column summary statistics on the samples in the training set.

The "unit std" is computed using the corrected sample standard deviation (https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation), which is computed as the square root of the unbiased sample variance.

Annotations
@Since("1.1.0")
Source
StandardScaler.scala
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Instance Constructors

  1. new StandardScaler()
    Annotations
    @Since("1.1.0")
  2. new StandardScaler(withMean: Boolean, withStd: Boolean)

    withMean

    False by default. Centers the data with mean before scaling. It will build a dense output, so take care when applying to sparse input.

    withStd

    True by default. Scales the data to unit standard deviation.

    Annotations
    @Since("1.1.0")

Type Members

  1. implicit class LogStringContext extends AnyRef
    Definition Classes
    Logging

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##: Int
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  4. def MDC(key: LogKey, value: Any): MDC
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  5. final def asInstanceOf[T0]: T0
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  6. def clone(): AnyRef
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  7. final def eq(arg0: AnyRef): Boolean
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  8. def equals(arg0: AnyRef): Boolean
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  9. def fit(data: RDD[Vector]): StandardScalerModel

    Computes the mean and variance and stores as a model to be used for later scaling.

    Computes the mean and variance and stores as a model to be used for later scaling.

    data

    The data used to compute the mean and variance to build the transformation model.

    returns

    a StandardScalarModel

    Annotations
    @Since("1.1.0")
  10. final def getClass(): Class[_ <: AnyRef]
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    @IntrinsicCandidate() @native()
  11. def hashCode(): Int
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  12. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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  13. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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  14. final def isInstanceOf[T0]: Boolean
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  15. def isTraceEnabled(): Boolean
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  16. def log: Logger
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  17. def logBasedOnLevel(level: Level)(f: => MessageWithContext): Unit
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  18. def logDebug(msg: => String, throwable: Throwable): Unit
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  19. def logDebug(entry: LogEntry, throwable: Throwable): Unit
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  20. def logDebug(entry: LogEntry): Unit
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  21. def logDebug(msg: => String): Unit
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  22. def logError(msg: => String, throwable: Throwable): Unit
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  23. def logError(entry: LogEntry, throwable: Throwable): Unit
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  24. def logError(entry: LogEntry): Unit
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  25. def logError(msg: => String): Unit
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  26. def logInfo(msg: => String, throwable: Throwable): Unit
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  27. def logInfo(entry: LogEntry, throwable: Throwable): Unit
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  28. def logInfo(entry: LogEntry): Unit
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  29. def logInfo(msg: => String): Unit
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  30. def logName: String
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  31. def logTrace(msg: => String, throwable: Throwable): Unit
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  32. def logTrace(entry: LogEntry, throwable: Throwable): Unit
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  33. def logTrace(entry: LogEntry): Unit
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  34. def logTrace(msg: => String): Unit
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  35. def logWarning(msg: => String, throwable: Throwable): Unit
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  36. def logWarning(entry: LogEntry, throwable: Throwable): Unit
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  37. def logWarning(entry: LogEntry): Unit
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  38. def logWarning(msg: => String): Unit
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  39. final def ne(arg0: AnyRef): Boolean
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  40. final def notify(): Unit
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  41. final def notifyAll(): Unit
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  42. final def synchronized[T0](arg0: => T0): T0
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  43. def toString(): String
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  44. final def wait(arg0: Long, arg1: Int): Unit
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  45. final def wait(arg0: Long): Unit
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  46. final def wait(): Unit
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  47. def withLogContext(context: Map[String, String])(body: => Unit): Unit
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Deprecated Value Members

  1. def finalize(): Unit
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    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

Inherited from Logging

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