Github user mengxr commented on a diff in the pull request:

    https://github.com/apache/spark/pull/4274#discussion_r23832172
  
    --- Diff: 
mllib/src/main/scala/org/apache/spark/mllib/linalg/distributed/BlockMatrix.scala
 ---
    @@ -237,4 +239,88 @@ class BlockMatrix(
         val localMat = toLocalMatrix()
         new BDM[Double](localMat.numRows, localMat.numCols, localMat.toArray)
       }
    +
    +  /** Adds two block matrices together. The matrices must have the same 
size and matching
    +    * `rowsPerBlock` and `colsPerBlock` values. */
    +  def add(other: BlockMatrix): BlockMatrix = {
    +    require(numRows() == other.numRows(), "Both matrices must have the 
same number of rows. " +
    +      s"A.numRows: ${numRows()}, B.numRows: ${other.numRows()}")
    +    require(numCols() == other.numCols(), "Both matrices must have the 
same number of columns. " +
    +      s"A.numCols: ${numCols()}, B.numCols: ${other.numCols()}")
    +    if (checkPartitioning(other, OperationNames.add)) {
    +      val addedBlocks = blocks.cogroup(other.blocks, partitioner).
    +        map { case ((blockRowIndex, blockColIndex), (a, b)) =>
    +          if (a.isEmpty) {
    +            new MatrixBlock((blockRowIndex, blockColIndex), b.head)
    +          } else if (b.isEmpty) {
    +            new MatrixBlock((blockRowIndex, blockColIndex), a.head)
    +          } else {
    +            val result = a.head.toBreeze + b.head.toBreeze
    +            new MatrixBlock((blockRowIndex, blockColIndex), 
Matrices.fromBreeze(result))
    +          }
    +      }
    +      new BlockMatrix(addedBlocks, rowsPerBlock, colsPerBlock, numRows(), 
numCols())
    +    } else {
    +      throw new SparkException(
    +        "Cannot add matrices with non-matching partitioners")
    +    }
    +  }
    +
    +  /** Left multiplies this [[BlockMatrix]] to `other`, another 
[[BlockMatrix]]. The `colsPerBlock`
    +    * of this matrix must equal the `rowsPerBlock` of `other`. If `other` 
contains
    +    * [[SparseMatrix]], they will have to be converted to a
    +    * [[DenseMatrix]]. This may cause some performance issues until 
support for multiplying
    +    * two sparse matrices is added.
    +    */
    +  def multiply(other: BlockMatrix): BlockMatrix = {
    +    require(numCols() == other.numRows(), "The number of columns of A and 
the number of rows " +
    +      s"of B must be equal. A.numCols: ${numCols()}, B.numRows: 
${other.numRows()}. If you " +
    +      s"think they should be equal, try setting the dimensions of A and B 
explicitly while " +
    +      s"initializing them.")
    +    if (checkPartitioning(other, OperationNames.multiply)) {
    --- End diff --
    
    Same here. We can embedded the logic here.


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