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 HashingTF extends Serializable

Maps a sequence of terms to their term frequencies using the hashing trick.

Annotations
@Since("1.1.0")
Source
HashingTF.scala
Linear Supertypes
Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. HashingTF
  2. Serializable
  3. AnyRef
  4. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new HashingTF()

    Annotations
    @Since("1.1.0")
  2. new HashingTF(numFeatures: Int)

    numFeatures

    number of features (default: 220)

Value Members

  1. def indexOf(term: Any): Int

    Returns the index of the input term.

    Returns the index of the input term.

    Annotations
    @Since("1.1.0")
  2. val numFeatures: Int
  3. def setBinary(value: Boolean): HashingTF.this.type

    If true, term frequency vector will be binary such that non-zero term counts will be set to 1 (default: false)

    If true, term frequency vector will be binary such that non-zero term counts will be set to 1 (default: false)

    Annotations
    @Since("2.0.0")
  4. def setHashAlgorithm(value: String): HashingTF.this.type

    Set the hash algorithm used when mapping term to integer.

    Set the hash algorithm used when mapping term to integer. (default: murmur3)

    Annotations
    @Since("2.0.0")
  5. def transform[D <: Iterable[_]](dataset: JavaRDD[D]): JavaRDD[Vector]

    Transforms the input document to term frequency vectors (Java version).

    Transforms the input document to term frequency vectors (Java version).

    Annotations
    @Since("1.1.0")
  6. def transform[D <: Iterable[_]](dataset: RDD[D]): RDD[Vector]

    Transforms the input document to term frequency vectors.

    Transforms the input document to term frequency vectors.

    Annotations
    @Since("1.1.0")
  7. def transform(document: Iterable[_]): Vector

    Transforms the input document into a sparse term frequency vector (Java version).

    Transforms the input document into a sparse term frequency vector (Java version).

    Annotations
    @Since("1.1.0")
  8. def transform(document: Iterable[_]): Vector

    Transforms the input document into a sparse term frequency vector.

    Transforms the input document into a sparse term frequency vector.

    Annotations
    @Since("1.1.0")