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 linalg
    Definition Classes
    mllib
  • package distributed
    Definition Classes
    linalg
  • DenseMatrix
  • DenseVector
  • Matrices
  • Matrix
  • QRDecomposition
  • SingularValueDecomposition
  • SparseMatrix
  • SparseVector
  • Vector
  • VectorUDT
  • Vectors

object Vectors

Factory methods for org.apache.spark.mllib.linalg.Vector. We don't use the name Vector because Scala imports scala.collection.immutable.Vector by default.

Annotations
@Since("1.0.0")
Source
Vectors.scala
Linear Supertypes
AnyRef, Any
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  1. Vectors
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Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @IntrinsicCandidate() @native()
  6. def dense(values: Array[Double]): Vector

    Creates a dense vector from a double array.

    Creates a dense vector from a double array.

    Annotations
    @Since("1.0.0")
  7. def dense(firstValue: Double, otherValues: Double*): Vector

    Creates a dense vector from its values.

    Creates a dense vector from its values.

    Annotations
    @Since("1.0.0") @varargs()
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  9. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  10. def fromJson(json: String): Vector

    Parses the JSON representation of a vector into a Vector.

    Parses the JSON representation of a vector into a Vector.

    Annotations
    @Since("1.6.0")
  11. def fromML(v: ml.linalg.Vector): Vector

    Convert new linalg type to spark.mllib type.

    Convert new linalg type to spark.mllib type. Light copy; only copies references

    Annotations
    @Since("2.0.0")
  12. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  13. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @IntrinsicCandidate() @native()
  14. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  15. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  16. def norm(vector: Vector, p: Double): Double

    Returns the p-norm of this vector.

    Returns the p-norm of this vector.

    vector

    input vector.

    p

    norm.

    returns

    norm in Lp space.

    Annotations
    @Since("1.3.0")
  17. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  18. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @IntrinsicCandidate() @native()
  19. def parse(s: String): Vector

    Parses a string resulted from Vector.toString into a Vector.

    Parses a string resulted from Vector.toString into a Vector.

    Annotations
    @Since("1.1.0")
  20. def sparse(size: Int, elements: Iterable[(Integer, Double)]): Vector

    Creates a sparse vector using unordered (index, value) pairs in a Java friendly way.

    Creates a sparse vector using unordered (index, value) pairs in a Java friendly way.

    size

    vector size.

    elements

    vector elements in (index, value) pairs.

    Annotations
    @Since("1.0.0")
  21. def sparse(size: Int, elements: Seq[(Int, Double)]): Vector

    Creates a sparse vector using unordered (index, value) pairs.

    Creates a sparse vector using unordered (index, value) pairs.

    size

    vector size.

    elements

    vector elements in (index, value) pairs.

    Annotations
    @Since("1.0.0")
  22. def sparse(size: Int, indices: Array[Int], values: Array[Double]): Vector

    Creates a sparse vector providing its index array and value array.

    Creates a sparse vector providing its index array and value array.

    size

    vector size.

    indices

    index array, must be strictly increasing.

    values

    value array, must have the same length as indices.

    Annotations
    @Since("1.0.0")
  23. def sqdist(v1: Vector, v2: Vector): Double

    Returns the squared distance between two Vectors.

    Returns the squared distance between two Vectors.

    v1

    first Vector.

    v2

    second Vector.

    returns

    squared distance between two Vectors.

    Annotations
    @Since("1.3.0")
  24. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  25. def toString(): String
    Definition Classes
    AnyRef → Any
  26. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  27. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  28. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  29. def zeros(size: Int): Vector

    Creates a vector of all zeros.

    Creates a vector of all zeros.

    size

    vector size

    returns

    a zero vector

    Annotations
    @Since("1.1.0")

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

    (Since version 9)

Inherited from AnyRef

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