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 Matrices

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

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@Since( "1.0.0" )
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  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
    Attributes
    protected[java.lang]
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    @native() @throws( ... )
  6. def dense(numRows: Int, numCols: Int, values: Array[Double]): Matrix

    Creates a column-major dense matrix.

    Creates a column-major dense matrix.

    numRows

    number of rows

    numCols

    number of columns

    values

    matrix entries in column major

    Annotations
    @Since( "1.0.0" )
  7. def diag(vector: Vector): Matrix

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

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

    vector

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

    returns

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

    Annotations
    @Since( "1.2.0" )
  8. final def eq(arg0: AnyRef): Boolean
    Definition Classes
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  9. def equals(arg0: Any): Boolean
    Definition Classes
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  10. def eye(n: Int): Matrix

    Generate a dense Identity Matrix in Matrix format.

    Generate a dense Identity Matrix in Matrix format.

    n

    number of rows and columns of the matrix

    returns

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

    Annotations
    @Since( "1.2.0" )
  11. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
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    @throws( classOf[java.lang.Throwable] )
  12. def fromML(m: ml.linalg.Matrix): Matrix

    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" )
  13. final def getClass(): Class[_]
    Definition Classes
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    @native()
  14. def hashCode(): Int
    Definition Classes
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    @native()
  15. def horzcat(matrices: Array[Matrix]): Matrix

    Horizontally concatenate a sequence of matrices.

    Horizontally concatenate a sequence of matrices. The returned matrix will be in the format the matrices are supplied in. Supplying a mix of dense and sparse matrices will result in a sparse matrix. If the Array is empty, an empty DenseMatrix will be returned.

    matrices

    array of matrices

    returns

    a single Matrix composed of the matrices that were horizontally concatenated

    Annotations
    @Since( "1.3.0" )
  16. final def isInstanceOf[T0]: Boolean
    Definition Classes
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  17. final def ne(arg0: AnyRef): Boolean
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  18. final def notify(): Unit
    Definition Classes
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    @native()
  19. final def notifyAll(): Unit
    Definition Classes
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    @native()
  20. def ones(numRows: Int, numCols: Int): Matrix

    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

    Matrix with size numRows x numCols and values of ones

    Annotations
    @Since( "1.2.0" )
  21. def rand(numRows: Int, numCols: Int, rng: Random): Matrix

    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

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

    Annotations
    @Since( "1.2.0" )
  22. def randn(numRows: Int, numCols: Int, rng: Random): Matrix

    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

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

    Annotations
    @Since( "1.2.0" )
  23. def sparse(numRows: Int, numCols: Int, colPtrs: Array[Int], rowIndices: Array[Int], values: Array[Double]): Matrix

    Creates a column-major sparse matrix in Compressed Sparse Column (CSC) format.

    Creates a column-major sparse matrix in Compressed Sparse Column (CSC) format.

    numRows

    number of rows

    numCols

    number of columns

    colPtrs

    the index corresponding to the start of a new column

    rowIndices

    the row index of the entry

    values

    non-zero matrix entries in column major

    Annotations
    @Since( "1.2.0" )
  24. def speye(n: Int): Matrix

    Generate a sparse Identity Matrix in Matrix format.

    Generate a sparse Identity Matrix in Matrix format.

    n

    number of rows and columns of the matrix

    returns

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

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

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

    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

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

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

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

    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

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

    Annotations
    @Since( "1.3.0" )
  27. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
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  28. def toString(): String
    Definition Classes
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  29. def vertcat(matrices: Array[Matrix]): Matrix

    Vertically concatenate a sequence of matrices.

    Vertically concatenate a sequence of matrices. The returned matrix will be in the format the matrices are supplied in. Supplying a mix of dense and sparse matrices will result in a sparse matrix. If the Array is empty, an empty DenseMatrix will be returned.

    matrices

    array of matrices

    returns

    a single Matrix composed of the matrices that were vertically concatenated

    Annotations
    @Since( "1.3.0" )
  30. final def wait(): Unit
    Definition Classes
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    @throws( ... )
  31. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
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  32. final def wait(arg0: Long): Unit
    Definition Classes
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    @native() @throws( ... )
  33. def zeros(numRows: Int, numCols: Int): Matrix

    Generate a Matrix consisting of zeros.

    Generate a Matrix consisting of zeros.

    numRows

    number of rows of the matrix

    numCols

    number of columns of the matrix

    returns

    Matrix with size numRows x numCols and values of zeros

    Annotations
    @Since( "1.2.0" )

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