Computing the svd with the stochastic projection is your best bet.
Sent from my iPhone
On Oct 17, 2012, at 10:42 PM, Ranjith Uthaman
wrote:
> Hi,
>
> Does map reduce implementation of Pseudo-Inverse of a matrix exist in the
> current Mahout framework? What are the various ways to achieve it
I asked in reply on Quora -- what exactly are you computing? what is
the size of input and are you talking about a generalized inverse.
Depending on this there are easier ways than an SVD.
On Thu, Oct 18, 2012 at 6:42 AM, Ranjith Uthaman
wrote:
> Hi,
>
> Does map reduce implementation of Pseudo-I
Hello Kris,
We have an example of how to use ALS to factorize the movielens dataset,
which also includes generating recommendations from the factorization:
examples/bin/factorize-movielens-1M.sh
We don't have automated code to find appropriate values for alpha and
lambda unfortunately. There is
alpha matters almost not at all by itself. It is used to create a
weight in the cost function of 1 + alpha * rating. Except for the "1
+", it would not matter as it just scales all weights in the first
half of the cost function proportionally.
Where it does matter is in relation to lambda, because
> I don't know if there is code,
> probably not, but conceptually that is all that it involves.
Once you factorized your interaction matrix, you can use
org.apache.mahout.cf.taste.hadoop.als.RecommenderJob
to compute recommendations in parallel.
Best,
Sebastian
The final pursuit is building a content-based recommender of the item for each
user. User-based and item-based recommenders of mahout as discussed in
MahoutInAction book doesn't fare very well considering the data available.
Also, a content-based recommender approach is also hinted in the book.
So you have a factorization like A = X * Y' and you are looking for
the right inverse of Y' (where Y is the item-feature matrix)?
This is just Y * pinv(Y' * Y). Y' * Y takes a little work to compute,
but can be done in one pass over the matrix. Y' * Y is just a
1000x1000 matrix which you can inver
For Mahout 0.7, the format of the model files for LDA are just a
SequenceFile, with the row numbers being the
topicIds, and the entries being the (un-normalized) probabilities for each
termId.
bin/vectordump --dictionary \
--dictioanryType \
--in
I am running the lda for the first time. I gave the following command to
test over the Reuters dataset but i got the error
lda -i reuters-vectors/tf-vectors -o reuters-lda-sparse -k 10 -v 7000 -x
20 -ow
hadoop binary is not in PATH,HADOOP_HOME/bin,HADOOP_PREFIX/bin, running
locally
SLF4J: C
Hi,
I'm using Hadoop-based and non-Hadoop-based recommender.
When I change only hadoop parameter "mapred.reduce.tasks",
the recommender result also changed.
(I cannot gain same result.)
Why is it happen?
Thanks
--
///=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
Y. Sakamoto
Some algorithms use a random seed, which could give different answers
on the same input. That's the first question.
On Thu, Oct 18, 2012 at 6:21 PM, Y. Sakamoto wrote:
> Hi,
>
> I'm using Hadoop-based and non-Hadoop-based recommender.
>
> When I change only hadoop parameter "mapred.reduce.tasks",
On Thu, Oct 18, 2012 at 9:16 AM, Vineeth wrote:
> I am running the lda for the first time. I gave the following command to
> test over the Reuters dataset but i got the error
>
> lda -i reuters-vectors/tf-vectors -o reuters-lda-sparse -k 10 -v 7000 -x
> 20 -ow
>
> hadoop binary is not in PATH,HAD
I'm working on a naive Bayes classifier in a case where a few
categories are much less common than the rest. In the latest run of
the process it happened that no instances of one of these ended up in
the test set. As a result testnb failed with the following error
(actual name of the label elide
Hi chiranjeevi,
were you able to solve this issue?
Thanks.
Congratulations Jake!
Jake Mannix has been recommended by the Mahout PMC to replace me as the
new Mahout PMC Chairperson and the Apache Board has approved this
recommendation in their meeting this week. Please join me in
congratulating Jake on his new role in this exciting and interesting
Apa
Team
Version Used : Mahout 0.6
Hadoop : 5 Nodes(1 Master + 4 Slaves)
Once we had generated kmean clusters for 60 documents.I had run the
clusterdump, which will extract the top terms from the cluster, There i had
noticed only one clusters is made even though we had specified the n
We previously did some k-means clustering runs on
different sized collections and noticed how that a large cluster was often
created
along with some smaller others. In digging deeper it turned out a lot of the
document vectors (produced via the seq2sparse command) were null (empty).
k-means appa
Thanks Jeff,
I'm happy to take up the mantle, send our mapper legions into the vast
uncharted lands of Big Data, reduce the unwashed noisy barbarians to clean
and convergent nuggets of intelligence and models to be launched into more
mappers! Or is this more like one of those "bully pulpit" pos
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