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

class SparseVector extends Vector

A sparse vector represented by an index array and a value array.

Annotations
@Since( "1.0.0" ) @SQLUserDefinedType()
Linear Supertypes
Vector, Serializable, Serializable, AnyRef, Any
Ordering
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Inherited
  1. SparseVector
  2. Vector
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new SparseVector(size: Int, indices: Array[Int], values: Array[Double])

    size

    size of the vector.

    indices

    index array, assume to be strictly increasing.

    values

    value array, must have the same length as the index array.

    Annotations
    @Since( "1.0.0" )

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. def apply(i: Int): Double

    Gets the value of the ith element.

    Gets the value of the ith element.

    i

    index

    Definition Classes
    Vector
    Annotations
    @Since( "1.1.0" )
  5. def argmax: Int

    Find the index of a maximal element.

    Find the index of a maximal element. Returns the first maximal element in case of a tie. Returns -1 if vector has length 0.

    Definition Classes
    SparseVector → Vector
    Annotations
    @Since( "1.5.0" )
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. def asML: ml.linalg.SparseVector

    Convert this vector to the new mllib-local representation.

    Convert this vector to the new mllib-local representation. This does NOT copy the data; it copies references.

    Definition Classes
    SparseVector → Vector
    Annotations
    @Since( "2.0.0" )
  8. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  9. def compressed: Vector

    Returns a vector in either dense or sparse format, whichever uses less storage.

    Returns a vector in either dense or sparse format, whichever uses less storage.

    Definition Classes
    Vector
    Annotations
    @Since( "1.4.0" )
  10. def copy: SparseVector

    Makes a deep copy of this vector.

    Makes a deep copy of this vector.

    Definition Classes
    SparseVector → Vector
    Annotations
    @Since( "1.1.0" )
  11. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  12. def equals(other: Any): Boolean
    Definition Classes
    SparseVector → Vector → AnyRef → Any
  13. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  14. def foreachActive(f: (Int, Double) ⇒ Unit): Unit

    Applies a function f to all the active elements of dense and sparse vector.

    Applies a function f to all the active elements of dense and sparse vector.

    f

    the function takes two parameters where the first parameter is the index of the vector with type Int, and the second parameter is the corresponding value with type Double.

    Definition Classes
    SparseVector → Vector
    Annotations
    @Since( "1.6.0" )
  15. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  16. def hashCode(): Int

    Returns a hash code value for the vector.

    Returns a hash code value for the vector. The hash code is based on its size and its first 128 nonzero entries, using a hash algorithm similar to java.util.Arrays.hashCode.

    Definition Classes
    SparseVector → Vector → AnyRef → Any
  17. val indices: Array[Int]
    Annotations
    @Since( "1.0.0" )
  18. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  19. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  20. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  21. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  22. def numActives: Int

    Number of active entries.

    Number of active entries. An "active entry" is an element which is explicitly stored, regardless of its value.

    Definition Classes
    SparseVector → Vector
    Annotations
    @Since( "1.4.0" )
    Note

    Inactive entries have value 0.

  23. def numNonzeros: Int

    Number of nonzero elements.

    Number of nonzero elements. This scans all active values and count nonzeros.

    Definition Classes
    SparseVector → Vector
    Annotations
    @Since( "1.4.0" )
  24. val size: Int

    Size of the vector.

    Size of the vector.

    Definition Classes
    SparseVector → Vector
    Annotations
    @Since( "1.0.0" )
  25. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  26. def toArray: Array[Double]

    Converts the instance to a double array.

    Converts the instance to a double array.

    Definition Classes
    SparseVector → Vector
    Annotations
    @Since( "1.0.0" )
  27. def toDense: DenseVector

    Converts this vector to a dense vector.

    Converts this vector to a dense vector.

    Definition Classes
    Vector
    Annotations
    @Since( "1.4.0" )
  28. def toJson: String

    Converts the vector to a JSON string.

    Converts the vector to a JSON string.

    Definition Classes
    SparseVector → Vector
    Annotations
    @Since( "1.6.0" )
  29. def toSparse: SparseVector

    Converts this vector to a sparse vector with all explicit zeros removed.

    Converts this vector to a sparse vector with all explicit zeros removed.

    Definition Classes
    Vector
    Annotations
    @Since( "1.4.0" )
  30. def toString(): String
    Definition Classes
    SparseVector → AnyRef → Any
  31. val values: Array[Double]
    Annotations
    @Since( "1.0.0" )
  32. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  33. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  34. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )

Inherited from Vector

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped