Class

org.apache.spark.mllib.linalg.distributed

IndexedRowMatrix

Related Doc: package distributed

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class IndexedRowMatrix extends DistributedMatrix

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

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@Since( "1.0.0" )
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DistributedMatrix, Serializable, Serializable, AnyRef, Any
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  1. IndexedRowMatrix
  2. DistributedMatrix
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Instance Constructors

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

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    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)

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    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

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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

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    protected[java.lang]
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    @throws( ... )
  6. def columnSimilarities(): CoordinateMatrix

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    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

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    Computes the Gramian matrix A^T A.

    Computes the Gramian matrix A^T A.

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    @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]

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    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

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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    @throws( classOf[java.lang.Throwable] )
  12. final def getClass(): Class[_]

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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  15. def multiply(B: Matrix): IndexedRowMatrix

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    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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. def numCols(): Long

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    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

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    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]

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    indexed rows of this matrix

    indexed rows of this matrix

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

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  23. def toBlockMatrix(rowsPerBlock: Int, colsPerBlock: Int): BlockMatrix

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    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" )
  24. def toBlockMatrix(): BlockMatrix

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    Converts to BlockMatrix.

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

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

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

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

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    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

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  28. final def wait(): Unit

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    @throws( ... )
  29. final def wait(arg0: Long, arg1: Int): Unit

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  30. final def wait(arg0: Long): Unit

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Inherited from DistributedMatrix

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

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