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
  • BlockMatrix
  • CoordinateMatrix
  • DistributedMatrix
  • IndexedRow
  • IndexedRowMatrix
  • MatrixEntry
  • RowMatrix

class IndexedRowMatrix extends DistributedMatrix

Represents a row-oriented org.apache.spark.mllib.linalg.distributed.DistributedMatrix with indexed rows.

Annotations
@Since( "1.0.0" )
Source
IndexedRowMatrix.scala
Linear Supertypes
DistributedMatrix, Serializable, Serializable, AnyRef, Any
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Inherited
  1. IndexedRowMatrix
  2. DistributedMatrix
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new IndexedRowMatrix(rows: RDD[IndexedRow])

    Alternative constructor leaving matrix dimensions to be determined automatically.

    Alternative constructor leaving matrix dimensions to be determined automatically.

    Annotations
    @Since( "1.0.0" )
  2. new IndexedRowMatrix(rows: RDD[IndexedRow], nRows: Long, nCols: Int)

    rows

    indexed rows of this matrix

    nRows

    number of rows. A non-positive value means unknown, and then the number of rows will be determined by the max row index plus one.

    nCols

    number of columns. A non-positive value means unknown, and then the number of columns will be determined by the size of the first row.

    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. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native() @IntrinsicCandidate()
  6. def columnSimilarities(): CoordinateMatrix

    Compute all cosine similarities between columns of this matrix using the brute-force approach of computing normalized dot products.

    Compute all cosine similarities between columns of this matrix using the brute-force approach of computing normalized dot products.

    returns

    An n x n sparse upper-triangular matrix of cosine similarities between columns of this matrix.

    Annotations
    @Since( "1.6.0" )
  7. def computeGramianMatrix(): Matrix

    Computes the Gramian matrix A^T A.

    Computes the Gramian matrix A^T A.

    Annotations
    @Since( "1.0.0" )
    Note

    This cannot be computed on matrices with more than 65535 columns.

  8. def computeSVD(k: Int, computeU: Boolean = false, rCond: Double = 1e-9): SingularValueDecomposition[IndexedRowMatrix, Matrix]

    Computes the singular value decomposition of this IndexedRowMatrix.

    Computes the singular value decomposition of this IndexedRowMatrix. Denote this matrix by A (m x n), this will compute matrices U, S, V such that A = U * S * V'.

    The cost and implementation of this method is identical to that in org.apache.spark.mllib.linalg.distributed.RowMatrix With the addition of indices.

    At most k largest non-zero singular values and associated vectors are returned. If there are k such values, then the dimensions of the return will be:

    U is an org.apache.spark.mllib.linalg.distributed.IndexedRowMatrix of size m x k that satisfies U'U = eye(k), s is a Vector of size k, holding the singular values in descending order, and V is a local Matrix of size n x k that satisfies V'V = eye(k).

    k

    number of singular values to keep. We might return less than k if there are numerically zero singular values. See rCond.

    computeU

    whether to compute U

    rCond

    the reciprocal condition number. All singular values smaller than rCond * sigma(0) are treated as zero, where sigma(0) is the largest singular value.

    returns

    SingularValueDecomposition(U, s, V)

    Annotations
    @Since( "1.0.0" )
  9. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  11. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  12. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @IntrinsicCandidate()
  13. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  14. def multiply(B: Matrix): IndexedRowMatrix

    Multiply this matrix by a local matrix on the right.

    Multiply this matrix by a local matrix on the right.

    B

    a local matrix whose number of rows must match the number of columns of this matrix

    returns

    an IndexedRowMatrix representing the product, which preserves partitioning

    Annotations
    @Since( "1.0.0" )
  15. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  16. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  17. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @IntrinsicCandidate()
  18. def numCols(): Long

    Gets or computes the number of columns.

    Gets or computes the number of columns.

    Definition Classes
    IndexedRowMatrix → DistributedMatrix
    Annotations
    @Since( "1.0.0" )
  19. def numRows(): Long

    Gets or computes the number of rows.

    Gets or computes the number of rows.

    Definition Classes
    IndexedRowMatrix → DistributedMatrix
    Annotations
    @Since( "1.0.0" )
  20. val rows: RDD[IndexedRow]
    Annotations
    @Since( "1.0.0" )
  21. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  22. def toBlockMatrix(rowsPerBlock: Int, colsPerBlock: Int): BlockMatrix

    Converts to BlockMatrix.

    Converts to BlockMatrix. Blocks may be sparse or dense depending on the sparsity of the rows.

    rowsPerBlock

    The number of rows of each block. The blocks at the bottom edge may have a smaller value. Must be an integer value greater than 0.

    colsPerBlock

    The number of columns of each block. The blocks at the right edge may have a smaller value. Must be an integer value greater than 0.

    returns

    a BlockMatrix

    Annotations
    @Since( "1.3.0" )
  23. def toBlockMatrix(): BlockMatrix

    Converts to BlockMatrix.

    Converts to BlockMatrix. Creates blocks with size 1024 x 1024.

    Annotations
    @Since( "1.3.0" )
  24. def toCoordinateMatrix(): CoordinateMatrix

    Converts this matrix to a org.apache.spark.mllib.linalg.distributed.CoordinateMatrix.

    Converts this matrix to a org.apache.spark.mllib.linalg.distributed.CoordinateMatrix.

    Annotations
    @Since( "1.3.0" )
  25. def toRowMatrix(): RowMatrix

    Drops row indices and converts this matrix to a org.apache.spark.mllib.linalg.distributed.RowMatrix.

    Drops row indices and converts this matrix to a org.apache.spark.mllib.linalg.distributed.RowMatrix.

    Annotations
    @Since( "1.0.0" )
  26. def toString(): String
    Definition Classes
    AnyRef → Any
  27. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  28. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  29. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Deprecated Value Members

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

Inherited from DistributedMatrix

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

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