Class

org.apache.spark.mllib.linalg.distributed

IndexedRowMatrix

Related Doc: package distributed

Permalink

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
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. IndexedRowMatrix
  2. DistributedMatrix
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

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

    Permalink

    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)

    Permalink

    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

    Permalink
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0

    Permalink
    Definition Classes
    Any
  5. def clone(): AnyRef

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. def columnSimilarities(): CoordinateMatrix

    Permalink

    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

    Permalink

    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]

    Permalink

    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

    Permalink
    Definition Classes
    AnyRef
  10. def equals(arg0: Any): Boolean

    Permalink
    Definition Classes
    AnyRef → Any
  11. def finalize(): Unit

    Permalink
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  12. final def getClass(): Class[_]

    Permalink
    Definition Classes
    AnyRef → Any
  13. def hashCode(): Int

    Permalink
    Definition Classes
    AnyRef → Any
  14. final def isInstanceOf[T0]: Boolean

    Permalink
    Definition Classes
    Any
  15. def multiply(B: Matrix): IndexedRowMatrix

    Permalink

    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" )
  16. final def ne(arg0: AnyRef): Boolean

    Permalink
    Definition Classes
    AnyRef
  17. final def notify(): Unit

    Permalink
    Definition Classes
    AnyRef
  18. final def notifyAll(): Unit

    Permalink
    Definition Classes
    AnyRef
  19. def numCols(): Long

    Permalink

    Gets or computes the number of columns.

    Gets or computes the number of columns.

    Definition Classes
    IndexedRowMatrixDistributedMatrix
    Annotations
    @Since( "1.0.0" )
  20. def numRows(): Long

    Permalink

    Gets or computes the number of rows.

    Gets or computes the number of rows.

    Definition Classes
    IndexedRowMatrixDistributedMatrix
    Annotations
    @Since( "1.0.0" )
  21. val rows: RDD[IndexedRow]

    Permalink

    indexed rows of this matrix

    indexed rows of this matrix

    Annotations
    @Since( "1.0.0" )
  22. final def synchronized[T0](arg0: ⇒ T0): T0

    Permalink
    Definition Classes
    AnyRef
  23. def toBlockMatrix(rowsPerBlock: Int, colsPerBlock: Int): BlockMatrix

    Permalink

    Converts to BlockMatrix.

    Converts to BlockMatrix. Creates blocks of SparseMatrix.

    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" )
  24. def toBlockMatrix(): BlockMatrix

    Permalink

    Converts to BlockMatrix.

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

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

    Permalink

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

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

    Permalink

    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" )
  27. def toString(): String

    Permalink
    Definition Classes
    AnyRef → Any
  28. final def wait(): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  29. final def wait(arg0: Long, arg1: Int): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  30. final def wait(arg0: Long): Unit

    Permalink
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from DistributedMatrix

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped