I'd say work your way through that class and follow along with what it
does; I don't know of any documents like that beyond the code and what's on
the Mahout web site at http://mahout.apache.org.
On Wednesday, February 3, 2016, Mahmood Naderan
wrote:
> Really thanks for that. I am getting closer
Really thanks for that. I am getting closer to what I was searching for...
Is there any high level document about the procedure of the classifier (using
map reduce) after the training phase. For example:
1- Reading chunks
2- Sorting each chunk
3-...
I didn't find such an example on the web. Maybe
Here are a bunch
https://github.com/apache/mahout/tree/master/math/src/main/java/org/apache/mahout/math
Large matrices are typical, often on the order of hundreds of thousands to
millions of rows and hundreds of columns.
On Wed, Feb 3, 2016 at 11:21 AM, Mahmood N wrote:
> >The new code still us
>The new code still uses sparse and dense vectors and matrices, with local and
>distributed >iterators over rows and blocking into chunks of matrices as
>appropriate.
That is a good thing to know...
Regardless of the comparison, do you know where the most important data
structures are defined?
The new code still uses sparse and dense vectors and matrices, with local
and distributed iterators over rows and blocking into chunks of matrices as
appropriate.
You would be better off checking out the newest version from source (
https://github.com/apache/mahout) and taking a look since I won't
Dear Andrew,
Thanks for your reply. In fact, I need this information as part of my study on
some data analytics workloads. The benchmark had been setup about 3 years ago
by someone! What I really want to know is that how the software model (here the
code execution path) differs from regular de
Hi Mahmood, would be possible to trace the path out in an IDE like IntelliJ
but there's no automated method to print that out, if that's what you're
asking.
Definitely recommend upgrading as that's five major releases old if at all
possible.
Best
Andrew
On Wed, Feb 3, 2016 at 10:35 AM, Mahmood N
