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 SparseMatrix extends Serializable

Factory methods for org.apache.spark.mllib.linalg.SparseMatrix.

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@Since("1.3.0")
Source
Matrices.scala
Linear Supertypes
Serializable, AnyRef, Any
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  8. def fromCOO(numRows: Int, numCols: Int, entries: Iterable[(Int, Int, Double)]): SparseMatrix

    Generate a SparseMatrix from Coordinate List (COO) format.

    Generate a SparseMatrix from Coordinate List (COO) format. Input must be an array of (i, j, value) tuples. Entries that have duplicate values of i and j are added together. Tuples where value is equal to zero will be omitted.

    numRows

    number of rows of the matrix

    numCols

    number of columns of the matrix

    entries

    Array of (i, j, value) tuples

    returns

    The corresponding SparseMatrix

    Annotations
    @Since("1.3.0")
  9. def fromML(m: ml.linalg.SparseMatrix): SparseMatrix

    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")
  10. final def getClass(): Class[_ <: AnyRef]
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  15. final def notifyAll(): Unit
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  16. def spdiag(vector: Vector): SparseMatrix

    Generate a diagonal matrix in SparseMatrix format from the supplied values.

    Generate a diagonal matrix in SparseMatrix format from the supplied values.

    vector

    a Vector that will form the values on the diagonal of the matrix

    returns

    Square SparseMatrix with size values.length x values.length and non-zero values on the diagonal

    Annotations
    @Since("1.3.0")
  17. def speye(n: Int): SparseMatrix

    Generate an Identity Matrix in SparseMatrix format.

    Generate an Identity Matrix in SparseMatrix format.

    n

    number of rows and columns of the matrix

    returns

    SparseMatrix with size n x n and values of ones on the diagonal

    Annotations
    @Since("1.3.0")
  18. def sprand(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix

    Generate a SparseMatrix consisting of i.i.d.

    Generate a SparseMatrix consisting of i.i.d. uniform random numbers. The number of non-zero elements equal the ceiling of numRows x numCols x density

    numRows

    number of rows of the matrix

    numCols

    number of columns of the matrix

    density

    the desired density for the matrix

    rng

    a random number generator

    returns

    SparseMatrix with size numRows x numCols and values in U(0, 1)

    Annotations
    @Since("1.3.0")
  19. def sprandn(numRows: Int, numCols: Int, density: Double, rng: Random): SparseMatrix

    Generate a SparseMatrix consisting of i.i.d.

    Generate a SparseMatrix consisting of i.i.d. gaussian random numbers.

    numRows

    number of rows of the matrix

    numCols

    number of columns of the matrix

    density

    the desired density for the matrix

    rng

    a random number generator

    returns

    SparseMatrix with size numRows x numCols and values in N(0, 1)

    Annotations
    @Since("1.3.0")
  20. final def synchronized[T0](arg0: => T0): T0
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    (Since version 9)

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