You can always define an RDD transpose function yourself. This is what I use in
PySpark to transpose an RDD of numpy vectors. It’s not optimal and the vectors
need to fit in memory on the worker nodes.
def rddTranspose(rdd):
# add an index to the rows and the columns, result in triplet
dataT1 = data.zipWithIndex().flatMap(lambda (x,i): [(i,j,e) for (j,e) in
enumerate(x)])
# use the column from the original as key and group and sort
dataT2 = dataT1.map(lambda (i,j,e): (j, (i,e)))\
.groupByKey().sortByKey()
# Sort the lists inside the rows
dataT3 = dataT2.map(lambda (i, x): sorted(list(x), cmp=lambda
(i1,e1),(i2,e2): cmp(i1, i2)))
# Remove the indices inside the rows
dataT4 = dataT3.map(lambda x: map(lambda (i, y): y , x))
# convert to numpy arrays in the rows
return dataT4.map(lambda x: np.asarray(x))
Cheers,
Toni
On 12 Jan 2015 at 20:45:58, Alex Minnaar (aminn...@verticalscope.com) wrote:
That's not quite what I'm looking for. Let me provide an example. I have a
rowmatrix A that is nxm and I have two local matrices b and c. b is mx1 and c
is nx1. In my spark job I wish to perform the following two computations
A*b
and
A^T*c
I don't think this is possible without being able to transpose a rowmatrix. Am
I correct?
Thanks,
Alex
From: Reza Zadeh <r...@databricks.com>
Sent: Monday, January 12, 2015 1:58 PM
To: Alex Minnaar
Cc: u...@spark.incubator.apache.org
Subject: Re: RowMatrix multiplication
As you mentioned, you can perform A * b, where A is a rowmatrix and b is a
local matrix.
From your email, I figure you want to compute b * A^T. To do this, you can
compute C = A b^T, whose result is the transpose of what you were looking for,
i.e. C^T = b * A^T. To undo the transpose, you would have transpose C manually
yourself. Be careful though, because the result might not have each Row fit in
memory on a single machine, which is what RowMatrix requires. This danger is
why we didn't provide a transpose operation in RowMatrix natively.
To address this and more, there is an effort to provide more comprehensive
linear algebra through block matrices, which will likely make it to 1.3:
https://issues.apache.org/jira/browse/SPARK-3434
Best,
Reza
On Mon, Jan 12, 2015 at 6:33 AM, Alex Minnaar <aminn...@verticalscope.com>
wrote:
I have a rowMatrix on which I want to perform two multiplications. The first
is a right multiplication with a local matrix which is fine. But after that I
also wish to right multiply the transpose of my rowMatrix with a different
local matrix. I understand that there is no functionality to transpose a
rowMatrix at this time but I was wondering if anyone could suggest a any kind
of work-around for this. I had thought that I might be able to initially
create two rowMatrices - a normal version and a transposed version - and use
either when appropriate. Can anyone think of another alternative?
Thanks,
Alex
