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 DenseMatrix extends Serializable

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

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@Since( "1.3.0" )
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  1. final def !=(arg0: Any): Boolean
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  5. def clone(): AnyRef
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  6. def diag(vector: Vector): DenseMatrix

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

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

    vector

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

    returns

    Square DenseMatrix with size values.length x values.length and values on the diagonal

    Annotations
    @Since( "1.3.0" )
  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
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  8. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def eye(n: Int): DenseMatrix

    Generate an Identity Matrix in DenseMatrix format.

    Generate an Identity Matrix in DenseMatrix format.

    n

    number of rows and columns of the matrix

    returns

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

    Annotations
    @Since( "1.3.0" )
  10. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
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    @throws( classOf[java.lang.Throwable] )
  11. def fromML(m: ml.linalg.DenseMatrix): DenseMatrix

    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[_]
    Definition Classes
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  13. def hashCode(): Int
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  14. final def isInstanceOf[T0]: Boolean
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  15. final def ne(arg0: AnyRef): Boolean
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  16. final def notify(): Unit
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  17. final def notifyAll(): Unit
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    @native()
  18. def ones(numRows: Int, numCols: Int): DenseMatrix

    Generate a DenseMatrix consisting of ones.

    Generate a DenseMatrix consisting of ones.

    numRows

    number of rows of the matrix

    numCols

    number of columns of the matrix

    returns

    DenseMatrix with size numRows x numCols and values of ones

    Annotations
    @Since( "1.3.0" )
  19. def rand(numRows: Int, numCols: Int, rng: Random): DenseMatrix

    Generate a DenseMatrix consisting of i.i.d. uniform random numbers.

    Generate a DenseMatrix consisting of i.i.d. uniform random numbers.

    numRows

    number of rows of the matrix

    numCols

    number of columns of the matrix

    rng

    a random number generator

    returns

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

    Annotations
    @Since( "1.3.0" )
  20. def randn(numRows: Int, numCols: Int, rng: Random): DenseMatrix

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

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

    numRows

    number of rows of the matrix

    numCols

    number of columns of the matrix

    rng

    a random number generator

    returns

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

    Annotations
    @Since( "1.3.0" )
  21. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
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  22. def toString(): String
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  23. final def wait(): Unit
    Definition Classes
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    @throws( ... )
  24. final def wait(arg0: Long, arg1: Int): Unit
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  25. final def wait(arg0: Long): Unit
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    @native() @throws( ... )
  26. def zeros(numRows: Int, numCols: Int): DenseMatrix

    Generate a DenseMatrix consisting of zeros.

    Generate a DenseMatrix consisting of zeros.

    numRows

    number of rows of the matrix

    numCols

    number of columns of the matrix

    returns

    DenseMatrix with size numRows x numCols and values of zeros

    Annotations
    @Since( "1.3.0" )

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