Yes.
Now Spark API doesn't provide transpose function. You have to define it
like below.
def transpose(m: Array[Array[Double]]): Array[Array[Double]] = {
(for {
c <- m(0).indices
} yield m.map(_(c)) ).toArray
}
xj @ Tokyo
On Thu, Aug 21, 2014 at 10:12 PM, phoenix bai <mingzhi...@gmail.com> wrote:
> this is exactly what I was looking for. thank you.
>
> one thing though, it doesn`t have transpose() function defined, so I have
> to do the transpose myself for the localMat in your case.
> hoping it will be supported in the future :-)
>
>
>
> On Thu, Aug 21, 2014 at 7:30 PM, x <wasedax...@gmail.com> wrote:
>
>> You could create a distributed matrix with RowMatrix.
>>
>> val rmat = new RowMatrix(rows)
>>
>> And then make a local DenseMatrix.
>>
>> val localMat = Matrices.dense(m, n, mat)
>>
>> Then multiply them.
>>
>> rmat.multiply(localMat)
>>
>>
>> xj @ Tokyo
>>
>> On Thu, Aug 21, 2014 at 6:37 PM, Sean Owen <so...@cloudera.com> wrote:
>>
>>> Are you trying to multiply dense or sparse matrices? if sparse, are
>>> they very large -- meaning, are you looking for distributed
>>> operations?
>>>
>>> On Thu, Aug 21, 2014 at 10:07 AM, phoenix bai <mingzhi...@gmail.com>
>>> wrote:
>>> > there is RowMatrix implemented in spark.
>>> > and I check for a while but failed to find any matrix operations (like
>>> > multiplication etc) are defined in the class yet.
>>> >
>>> > so, my question is, if I want to do matrix multiplication, (to do
>>> vector x
>>> > matrix multiplication to be precise), need to convert the
>>> vector/matrix to
>>> > the the matrix type defined in breeze package right?
>>> >
>>> > thanks
>>>
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>>
>
