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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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

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10.  def equals(arg0: Any): Boolean

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11.  def finalize(): Unit

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12.  final def getClass(): Class[_]

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    @native()

13.  def hashCode(): Int

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14.  final def isInstanceOf[T0]: Boolean

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15.  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" )

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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    @native()

19.  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" )

20.  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" )

21.  val rows: RDD[IndexedRow]

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    @Since( "1.0.0" )

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

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23.  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" )

24.  def toBlockMatrix(): BlockMatrix

    Converts to BlockMatrix.

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

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    @Since( "1.3.0" )

25.  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" )

26.  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" )

27.  def toString(): String

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28.  final def wait(): Unit

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

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    @throws( ... )

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

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