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
c

org.apache.spark.mllib.feature

StandardScalerModel

class StandardScalerModel extends VectorTransformer

Represents a StandardScaler model that can transform vectors.

Annotations
@Since( "1.1.0" )
Source
StandardScaler.scala
Linear Supertypes
VectorTransformer, Serializable, Serializable, AnyRef, Any
Ordering
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Inherited
  1. StandardScalerModel
  2. VectorTransformer
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new StandardScalerModel(std: Vector)
    Annotations
    @Since( "1.3.0" )
  2. new StandardScalerModel(std: Vector, mean: Vector)

    Annotations
    @Since( "1.3.0" )
  3. new StandardScalerModel(std: Vector, mean: Vector, withStd: Boolean, withMean: Boolean)

    std

    column standard deviation values

    mean

    column mean values

    withStd

    whether to scale the data to have unit standard deviation

    withMean

    whether to center the data before scaling

    Annotations
    @Since( "1.3.0" )

Value Members

  1. val mean: Vector
    Annotations
    @Since( "1.1.0" )
  2. def setWithMean(withMean: Boolean): StandardScalerModel.this.type
    Annotations
    @Since( "1.3.0" )
  3. def setWithStd(withStd: Boolean): StandardScalerModel.this.type
    Annotations
    @Since( "1.3.0" )
  4. val std: Vector
    Annotations
    @Since( "1.3.0" )
  5. def transform(vector: Vector): Vector

    Applies standardization transformation on a vector.

    Applies standardization transformation on a vector.

    vector

    Vector to be standardized.

    returns

    Standardized vector. If the std of a column is zero, it will return default 0.0 for the column with zero std.

    Definition Classes
    StandardScalerModel → VectorTransformer
    Annotations
    @Since( "1.1.0" )
  6. def transform(data: JavaRDD[Vector]): JavaRDD[Vector]

    Applies transformation on a JavaRDD[Vector].

    Applies transformation on a JavaRDD[Vector].

    data

    JavaRDD[Vector] to be transformed.

    returns

    transformed JavaRDD[Vector].

    Definition Classes
    VectorTransformer
    Annotations
    @Since( "1.1.0" )
  7. def transform(data: RDD[Vector]): RDD[Vector]

    Applies transformation on an RDD[Vector].

    Applies transformation on an RDD[Vector].

    data

    RDD[Vector] to be transformed.

    returns

    transformed RDD[Vector].

    Definition Classes
    VectorTransformer
    Annotations
    @Since( "1.1.0" )
  8. var withMean: Boolean
    Annotations
    @Since( "1.3.0" )
  9. var withStd: Boolean
    Annotations
    @Since( "1.3.0" )