IndexedRowMatrix

Value Members

1. final def !=(arg0: AnyRef): Boolean

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2. final def !=(arg0: Any): Boolean

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3. final def ##(): Int

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4. final def ==(arg0: AnyRef): Boolean

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5. final def ==(arg0: Any): Boolean

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6. final def asInstanceOf[T0]: T0

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7. def clone(): AnyRef

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   protected[java.lang]

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8. def computeGramianMatrix(): Matrix

   Computes the Gramian matrix A^T A.

   Computes the Gramian matrix A^T A.

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   @Since( "1.0.0" )

9. 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)

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   @Since( "1.0.0" )

10. final def eq(arg0: AnyRef): Boolean

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11. def equals(arg0: Any): Boolean

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12. def finalize(): Unit

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13. final def getClass(): Class[_]

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14. def hashCode(): Int

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15. final def isInstanceOf[T0]: Boolean

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16. 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

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    @Since( "1.0.0" )

17. final def ne(arg0: AnyRef): Boolean

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18. final def notify(): Unit

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19. final def notifyAll(): Unit

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20. def numCols(): Long

    Gets or computes the number of columns.

    Gets or computes the number of columns.

    Definition Classes

    IndexedRowMatrix → DistributedMatrix

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    @Since( "1.0.0" )

21. def numRows(): Long

    Gets or computes the number of rows.

    Gets or computes the number of rows.

    Definition Classes

    IndexedRowMatrix → DistributedMatrix

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    @Since( "1.0.0" )

22. val rows: RDD[IndexedRow]

    indexed rows of this matrix

    indexed rows of this matrix

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    @Since( "1.0.0" )

23. final def synchronized[T0](arg0: ⇒ T0): T0

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

    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

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    @Since( "1.3.0" )

25. def toBlockMatrix(): BlockMatrix

    Converts to BlockMatrix.

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

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    @Since( "1.3.0" )

26. 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.

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    @Since( "1.3.0" )

27. 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.

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    @Since( "1.0.0" )

28. def toString(): String

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29. final def wait(): Unit

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30. final def wait(arg0: Long, arg1: Int): Unit

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

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