@sowen.. i am looking for distributed operations, especially very large
sparse matrix x sparse matrix multiplication. what is the best way to
implement this in spark?
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am looking for distributed operations, especially very large
sparse matrix x sparse matrix multiplication. what is the best way to
implement this in spark?
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is the best way to
implement this in spark?
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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
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
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
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
