I.e. i guess you want to run kmeans directly on usigma output. On May 30, 2013 9:37 AM, "Dmitriy Lyubimov" <dlie...@gmail.com> wrote:
> I believe this flow describes how to use lanczos svd in mahout to arrive > at the same reduction as ssvd already provides with pca and USigma options > in one step. This flow is irrelevant when working with ssvd, it already > does it all internally for you. > On May 30, 2013 5:45 AM, "Rajesh Nikam" <rajeshni...@gmail.com> wrote: > >> Hi Suneel/Dmitriy, >> >> I got mahout-examples-0.8-SNAPSHOT-job.jar compiled from trunk. >> Now I got -us param as your mentioned for the input set working. >> >> Steps followed are: >> >> mahout arff.vector --input /mnt/cluster/t/PE_EXE/input-set.arff --output >> /user/hadoop/t/input-set-vector/ --dictOut /mnt/cluster/t/input-set-dict >> >> hadoop jar mahout-examples-0.8-SNAPSHOT-job.jar >> org.apache.mahout.math.hadoop.stochasticsvd.SSVDCli --input >> /user/hadoop/t/input-set-vector/ --output /user/hadoop/t/input-set-svd/ -k >> 50 --reduceTasks 2 -U true -V false -us true -ow >> >> Not able to understand what needs to be provided input to >> cleansvd/transpose/matrixmult as mentioned on following page, what needs >> to >> be used U/V/USigma and how. >> >> Again how to understand which features got in reduced matrix. >> >> https://cwiki.apache.org/MAHOUT/dimensional-reduction.html >> >> At a high level, the steps we're going to perform are: >> >> bin/mahout svd (original -> svdOut) >> bin/mahout cleansvd ... >> bin/mahout transpose svdOut -> svdT >> bin/mahout transpose original -> originalT >> bin/mahout matrixmult originalT svdT -> newMatrix >> bin/mahout kmeans newMatrix >> >> Thanks, >> >> Rajesh >> >> >> >> On Mon, May 27, 2013 at 11:31 AM, Suneel Marthi <suneel_mar...@yahoo.com >> >wrote: >> >> > Ahha, I see your problem now. >> > >> > The additional line in trunk was added as part of Mahout-1097 (long >> after >> > Mahout-0.7 release) and hence you wouldn't see the change in >> > mahout-examples-0.7-job.jar that you are working off of. This fix is >> > presently available in trunk (and will be part of Mahout-0.8). >> > >> > I would recommend to work off of trunk for now and u should be good. >> > >> > >> > >> > >> > ________________________________ >> > From: Rajesh Nikam <rajeshni...@gmail.com> >> > To: user@mahout.apache.org >> > Sent: Monday, May 27, 2013 1:52 AM >> > Subject: Re: Fwd: Re: convert input for SVD >> > >> > >> > Hi Dmitriy / Suneel, >> > >> > You are pointing me to the correct solution. However I see difference >> > options in source code downloaded from (mahout-trunk.zip) and >> > mahout-examples-0.7-job.jar. >> > >> > Could you please verify the same at your end. >> > >> > ==>> from mahout-trunk.zip <<== >> > >> > addOption("uHalfSigma", >> > "uhs", >> > "Compute U * Sigma^0.5", >> > String.valueOf(false)); >> > * addOption("uSigma", "us", "Compute U * Sigma", >> > String.valueOf(false));* >> > addOption("computeV", "V", "compute V (true/false)", >> > String.valueOf(true)); >> > >> > >> > ==>> mahout-examples-0.7-job.jar <<== >> > >> > addOption("uHalfSigma", "uhs", "Compute U as UHat=U x >> pow(Sigma,0.5)", >> > String.valueOf(false)); >> > >> > addOption("computeV", "V", "compute V (true/false)", >> > String.valueOf(true)); >> > addOption("vHalfSigma", "vhs", "compute V as VHat= V x >> pow(Sigma,0.5)", >> > String.valueOf(false)); >> > >> > >> > Thanks, >> > Rajesh >> > >> > >> > On Fri, May 24, 2013 at 10:48 PM, Dmitriy Lyubimov <dlie...@gmail.com >> > >wrote: >> > >> > > "ssvd -us true...." should do this . Suneel says it still works on >> trunk. >> > > >> > > >> > > On Fri, May 24, 2013 at 9:38 AM, Rajesh Nikam <rajeshni...@gmail.com> >> > > wrote: >> > > >> > > > Thanks Dmitriy & Suneel for comments. As you suggested I need to >> use U >> > * >> > > > Sigma. >> > > > >> > > > It means Need to get multiplication of these matrices. >> > > > >> > > > Which Mahout props to use for this? >> > > > >> > > > Other question was how to get features that are selected in U? >> > > > On May 24, 2013 8:45 PM, "Suneel Marthi" <suneel_mar...@yahoo.com> >> > > wrote: >> > > > >> > > > > Rajesh, >> > > > > >> > > > > I am working off of trunk and this works fine. >> > > > > >> > > > > As Dmitriy says u do need USigma. >> > > > > >> > > > > It would help to paste the entire stacktrace you are seeing with >> > > > > MatrixColumnMeansJob. >> > > > > >> > > > > If you are still seeing an issue, I would suggest that you work >> off >> > of >> > > > > trunk. >> > > > > >> > > > > >> > > > > >> > > > > >> > > > > ________________________________ >> > > > > From: Dmitriy Lyubimov <dlie...@gmail.com> >> > > > > To: user@mahout.apache.org >> > > > > Sent: Friday, May 24, 2013 9:52 AM >> > > > > Subject: Re: Fwd: Re: convert input for SVD >> > > > > >> > > > > >> > > > > I think last time i verified this flow was as of >> > > > > https://issues.apache.org/jira/browse/MAHOUT-1097. It was woking >> > then. >> > > > Did >> > > > > not look at it since. >> > > > > On May 24, 2013 6:42 AM, "Dmitriy Lyubimov" <dlie...@gmail.com> >> > wrote: >> > > > > >> > > > > > Rajesh, you will get more help if you stay on the list. >> > > > > > >> > > > > > you do need u *sigma output. there is no substitute. >> > > > > > >> > > > > > If this option is indeed no longer there, i have no knowledge of >> > it. >> > > > > Maybe >> > > > > > there was some work committed that screwed that but at the >> moment >> > i >> > > > have >> > > > > > no time to look at it. Obviously it was there at the time >> > > documentation >> > > > > was >> > > > > > written. I guess you may obtain an earlier snapshot as interim >> > > solution >> > > > > if >> > > > > > it is indeed the case. >> > > > > > >> > > > > > ---------- Forwarded message ---------- >> > > > > > From: "Rajesh Nikam" <rajeshni...@gmail.com> >> > > > > > Date: May 24, 2013 3:20 AM >> > > > > > Subject: Re: convert input for SVD >> > > > > > To: <user@mahout.apache.org> >> > > > > > Cc: >> > > > > > >> > > > > > > Hello Dmitriy, >> > > > > > > >> > > > > > > Thanks for reply. >> > > > > > > >> > > > > > > I see similar discussion on following link where I see your >> > reply. >> > > > > > > >> > > > > > > >> > > > > > >> > > > > >> > > > >> > > >> > >> http://www.searchworkings.org/forum/-/message_boards/view_message/517870#_19_message_519704 >> > > > > > > >> > > > > > > I do also have same problem, need to apply dimensionality >> > reduction >> > > > and >> > > > > > use >> > > > > > > clustering algo on reduced features. >> > > > > > > >> > > > > > > Seems parameters for ssvd are changed from mentioned in >> > > SSVD-CLI.pdf. >> > > > > It >> > > > > > no >> > > > > > > longer shows *-us *as parameter >> > > > > > > >> > > > > > > I am using mahout-examples-0.7-job.jar >> > > > > > > >> > > > > > > mahout ssvd --input /user/hadoop/t/input-set-vector/ --output >> > > > > > > /user/hadoop/t/input-set-svd/ -k 200 --reduceTasks 2 -pca >> true -U >> > > > true >> > > > > -V >> > > > > > > false *-us true* -ow -q 1 >> > > > > > > >> > > > > > > giving option as "*-pca true*" gives error as >> > > > > > > >> > > > > > > at >> > > > > > > >> > > > > > >> > > > > >> > > > >> > > >> > >> org.apache.mahout.math.hadoop.MatrixColumnMeansJob.run(MatrixColumnMeansJob.java:55) >> > > > > > > at >> > > > > > > >> > > > > > >> > > > > >> > > > >> > > >> > >> org.apache.mahout.math.hadoop.MatrixColumnMeansJob.run(MatrixColumnMeansJob.java:55) >> > > > > > > >> > > > > > > So I removed it. >> > > > > > > >> > > > > > > mahout ssvd --input /user/hadoop/t/input-set-vector/ --output >> > > > > > > /user/hadoop/t/input-set-svd/ -k 200 --reduceTasks 2 -U true >> -V >> > > false >> > > > > > *-us >> > > > > > > true* -ow -q 1 >> > > > > > > >> > > > > > > *>> *with above command *>> Unexpected -us *while processing >> > > > > Job-Specific >> > > > > > > Options. >> > > > > > > >> > > > > > > I tried with "-U false -V false -uhs true" it just generated >> > sigma >> > > > file >> > > > > > as >> > > > > > > expected however no "Usigma" >> > > > > > > >> > > > > > > hadoop fs -lsr /user/hadoop/t/PE_EXE/input-set-svd/ >> > > > > > > >> > > > > > > -rw-r--r-- 2 hadoop supergroup 1712 2013-05-24 15:34 >> > > > > > > /user/hadoop/t/PE_EXE/input-set-svd/sigma >> > > > > > > >> > > > > > > Then with *"-U true -V false -uhs true" *output dir U is >> created. >> > > > > > > * >> > > > > > > *drwxr-xr-x - hadoop supergroup 0 2013-05-24 15:39 >> > > > > > > /user/hadoop/t/PE_EXE/input-set-svd/U >> > > > > > > -rw-r--r-- 2 hadoop supergroup 1712 2013-05-24 15:39 >> > > > > > > /user/hadoop/t/PE_EXE/input-set-svd/sigma* >> > > > > > > * >> > > > > > > >> > > > > > > My problem is how to use these U/V/sigma file as input to >> > > > > canopy/kmeans ? >> > > > > > > >> > > > > > > How to identify which important features from U/Sigma that are >> > > > retained >> > > > > > in >> > > > > > > dimensionality reduction ? >> > > > > > > >> > > > > > > Thanks in Advance ! >> > > > > > > Rajesh >> > > > > > > >> > > > > > > >> > > > > > > On Fri, May 24, 2013 at 7:01 AM, Dmitriy Lyubimov < >> > > dlie...@gmail.com >> > > > > >> > > > > > wrote: >> > > > > > > >> > > > > > > > >> > > > > > > > >> > > > > > >> > > > > >> > > > >> > > >> > >> https://cwiki.apache.org/confluence/download/attachments/27832158/SSVD-CLI.pdf?version=17&modificationDate=1349999085000 >> > > > > > > > : >> > > > > > > > >> > > > > > > > "In most cases where you might be looking to reduce >> > > > > > > > dimensionality while retaining variance, you probably need >> > > > > combination >> > > > > > of >> > > > > > > > options -pca true -U false -V >> > > > > > > > false -us true. >> > > > > > > > >> > > > > > > > See ยง3 for details" >> > > > > > > > >> > > > > > > > >> > > > > > > > On Thu, May 23, 2013 at 6:24 PM, Dmitriy Lyubimov < >> > > > dlie...@gmail.com >> > > > > > >> > > > > > > > wrote: >> > > > > > > > >> > > > > > > > > Also, for the dimensionality reduction it is important >> among >> > > > other >> > > > > > things >> > > > > > > > > to re-center your input first, which is why you also want >> > "-pca >> > > > > > true". >> > > > > > > > > >> > > > > > > > > >> > > > > > > > > On Thu, May 23, 2013 at 6:23 PM, Dmitriy Lyubimov < >> > > > > dlie...@gmail.com >> > > > > > > > >wrote: >> > > > > > > > > >> > > > > > > > >> did you specify -us option? SSVD by default produces only >> > U, V >> > > > and >> > > > > > > > Sigma. >> > > > > > > > >> but it can produce more, e.g. U*Sigma, U*sqrt(Sigma) >> etc. if >> > > you >> > > > > > ask for >> > > > > > > > >> it. And, alternatively, you can suppress any of U, V (you >> > > can't >> > > > > > suppress >> > > > > > > > >> sigma but that doesn't cost anything in space anyway). >> > > > > > > > >> >> > > > > > > > >> >> > > > > > > > >> On Thu, May 23, 2013 at 6:20 PM, Rajesh Nikam < >> > > > > > rajeshni...@gmail.com >> > > > > > > > >wrote: >> > > > > > > > >> >> > > > > > > > >>> I got all three U, V & sigma from ssvd, however which to >> > use >> > > as >> > > > > > input >> > > > > > > > to >> > > > > > > > >>> canopy? >> > > > > > > > >>> On May 24, 2013 6:47 AM, "Dmitriy Lyubimov" < >> > > dlie...@gmail.com >> > > > > >> > > > > > wrote: >> > > > > > > > >>> >> > > > > > > > >>> > I think you want U*Sigma >> > > > > > > > >>> > >> > > > > > > > >>> > What you want is ssvd ... -pca true ... -us true ... >> see >> > > the >> > > > > > manual >> > > > > > > > >>> > >> > > > > > > > >>> > >> > > > > > > > >>> > >> > > > > > > > >>> > >> > > > > > > > >>> > On Thu, May 23, 2013 at 6:07 PM, Rajesh Nikam < >> > > > > > rajeshni...@gmail.com >> > > > > > > > > >> > > > > > > > >>> > wrote: >> > > > > > > > >>> > >> > > > > > > > >>> > > Sorry for confusion. Here number of clusters are >> > decided >> > > by >> > > > > > canopy. >> > > > > > > > >>> With >> > > > > > > > >>> > > data as it has 60 to 70 clusters. >> > > > > > > > >>> > > >> > > > > > > > >>> > > My question is which part from ssvd output U, V, >> Sigma >> > > > should >> > > > > > be >> > > > > > > > >>> used as >> > > > > > > > >>> > > input to canopy? >> > > > > > > > >>> > > On May 24, 2013 3:56 AM, "Ted Dunning" < >> > > > > ted.dunn...@gmail.com >> > > > > > > >> > > > > > > > >>> wrote: >> > > > > > > > >>> > > >> > > > > > > > >>> > > > Rajesh, >> > > > > > > > >>> > > > >> > > > > > > > >>> > > > This is very confusing. >> > > > > > > > >>> > > > >> > > > > > > > >>> > > > You have 1500 things that you are clustering into >> > more >> > > > than >> > > > > > 1400 >> > > > > > > > >>> > > clusters. >> > > > > > > > >>> > > > >> > > > > > > > >>> > > > There is no way for most of these clusters to >> have >1 >> > > > > member >> > > > > > just >> > > > > > > > >>> > because >> > > > > > > > >>> > > > there aren't enough clusters compared to the >> items. >> > > > > > > > >>> > > > >> > > > > > > > >>> > > > Is there a typo here? >> > > > > > > > >>> > > > >> > > > > > > > >>> > > > >> > > > > > > > >>> > > > >> > > > > > > > >>> > > > >> > > > > > > > >>> > > > On Thu, May 23, 2013 at 5:34 AM, Rajesh Nikam < >> > > > > > > > >>> rajeshni...@gmail.com> >> > > > > > > > >>> > > > wrote: >> > > > > > > > >>> > > > >> > > > > > > > >>> > > > > Hi, >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > I have input test set of 1500 instances with >> 1000+ >> > > > > > features. I >> > > > > > > > >>> want >> > > > > > > > >>> > to >> > > > > > > > >>> > > to >> > > > > > > > >>> > > > > SVD to reduce features. I have followed >> following >> > > steps >> > > > > > with >> > > > > > > > >>> generate >> > > > > > > > >>> > > > 1400+ >> > > > > > > > >>> > > > > clusters 99% of clusters contain 1 instance :( >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > Please let me know what is wrong in below steps >> - >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > mahout arff.vector --input >> > > > /mnt/cluster/t/input-set.arff >> > > > > > > > --output >> > > > > > > > >>> > > > > /user/hadoop/t/input-set-vector/ --dictOut >> > > > > > > > >>> > > /mnt/cluster/t/input-set-dict >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > mahout ssvd --input >> > /user/hadoop/t/input-set-vector/ >> > > > > > --output >> > > > > > > > >>> > > > > /user/hadoop/t/input-set-svd/ -k 200 >> --reduceTasks >> > 2 >> > > > -ow >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > mahout canopy -i >> */user/hadoop/t/input-set-svd/U* >> > -o >> > > > > > > > >>> > > > > /user/hadoop/t/input-set-canopy-centroids -dm >> > > > > > > > >>> > > > > >> > > > org.apache.mahout.common.distance.TanimotoDistanceMeasure >> > > > > > *-t1 >> > > > > > > > >>> 0.001 >> > > > > > > > >>> > > -t2 >> > > > > > > > >>> > > > > 0.002* >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > mahout kmeans -i >> */user/hadoop/t/input-set-svd/U* >> > -c >> > > > > > > > >>> > > > > >> > > > > /user/hadoop/t/input-set-canopy-centroids/clusters-0-final >> > > > > > -cl >> > > > > > > > -o >> > > > > > > > >>> > > > > /user/hadoop/t/input-set-kmeans-clusters -ow -x >> 10 >> > > -dm >> > > > > > > > >>> > > > > >> > > > org.apache.mahout.common.distance.TanimotoDistanceMeasure >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > mahout clusterdump -dt sequencefile -i >> > > > > > > > >>> > > > > >> > > > > /user/hadoop/t/input-set-kmeans-clusters/clusters-1-final/ >> > > > > > -n >> > > > > > > > 20 >> > > > > > > > >>> -b >> > > > > > > > >>> > 100 >> > > > > > > > >>> > > > -o >> > > > > > > > >>> > > > > /mnt/cluster/t/cdump-input-set.txt -p >> > > > > > > > >>> > > > > >> > > > /user/hadoop/t/input-set-kmeans-clusters/clusteredPoints/ >> > > > > > > > >>> --evaluate >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > Thanks in advance ! >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > Rajesh >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > On Wed, May 22, 2013 at 2:18 AM, Dmitriy >> Lyubimov < >> > > > > > > > >>> dlie...@gmail.com >> > > > > > > > >>> > > >> > > > > > > > >>> > > > > wrote: >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > > > PPS As far as the tool for arff, i am frankly >> not >> > > > sure. >> > > > > > but >> > > > > > > > it >> > > > > > > > >>> > sounds >> > > > > > > > >>> > > > > like >> > > > > > > > >>> > > > > > you've already solved this. >> > > > > > > > >>> > > > > > >> > > > > > > > >>> > > > > > >> > > > > > > > >>> > > > > > On Tue, May 21, 2013 at 1:41 PM, Dmitriy >> > Lyubimov < >> > > > > > > > >>> > dlie...@gmail.com >> > > > > > > > >>> > > > >> > > > > > > > >>> > > > > > wrote: >> > > > > > > > >>> > > > > > >> > > > > > > > >>> > > > > > > ps as far as U, V data "close to zero", yes >> > > that's >> > > > > what >> > > > > > > > you'd >> > > > > > > > >>> > > expect. >> > > > > > > > >>> > > > > > > >> > > > > > > > >>> > > > > > > Here, by "close to zero" it still means much >> > > bigger >> > > > > > than a >> > > > > > > > >>> > rounding >> > > > > > > > >>> > > > > error >> > > > > > > > >>> > > > > > > of course. e.g. 1E-12 is indeed a small >> number, >> > > and >> > > > > > 1E-16 >> > > > > > > > to >> > > > > > > > >>> > 1E-18 >> > > > > > > > >>> > > > > would >> > > > > > > > >>> > > > > > be >> > > > > > > > >>> > > > > > > indeed "close to zero" for the purposes of >> > > > > singularity. >> > > > > > > > >>> > 1E-2..1E-5 >> > > > > > > > >>> > > > are >> > > > > > > > >>> > > > > > > actually quite "sizeable" numbers by the >> scale >> > > of >> > > > > > IEEE 754 >> > > > > > > > >>> > > > > arithmetics. >> > > > > > > > >>> > > > > > > >> > > > > > > > >>> > > > > > > U and V are orthonormal (which means their >> > column >> > > > > > vectors >> > > > > > > > >>> have >> > > > > > > > >>> > > > > euclidiean >> > > > > > > > >>> > > > > > > norm of 1) . Note that for large m and n >> (large >> > > > > inputs) >> > > > > > > > they >> > > > > > > > >>> are >> > > > > > > > >>> > > also >> > > > > > > > >>> > > > > > > extremely skinny. The larger input is, the >> > > smaller >> > > > > the >> > > > > > > > >>> element >> > > > > > > > >>> > of U >> > > > > > > > >>> > > > > > or/and >> > > > > > > > >>> > > > > > > V is gonna be. >> > > > > > > > >>> > > > > > > >> > > > > > > > >>> > > > > > > >> > > > > > > > >>> > > > > > > >> > > > > > > > >>> > > > > > > On Tue, May 21, 2013 at 8:48 AM, Dmitriy >> > > Lyubimov < >> > > > > > > > >>> > > dlie...@gmail.com >> > > > > > > > >>> > > > > > >wrote: >> > > > > > > > >>> > > > > > > >> > > > > > > > >>> > > > > > >> Sounds like dimensionality reduction to me. >> > You >> > > > may >> > > > > > want >> > > > > > > > to >> > > > > > > > >>> use >> > > > > > > > >>> > > ssvd >> > > > > > > > >>> > > > > > -pca >> > > > > > > > >>> > > > > > >> >> > > > > > > > >>> > > > > > >> Apologies for brevity. Sent from my Android >> > > phone. >> > > > > > > > >>> > > > > > >> -Dmitriy >> > > > > > > > >>> > > > > > >> On May 21, 2013 6:27 AM, "Rajesh Nikam" < >> > > > > > > > >>> rajeshni...@gmail.com> >> > > > > > > > >>> > > > > wrote: >> > > > > > > > >>> > > > > > >> >> > > > > > > > >>> > > > > > >>> Hello Ted, >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> Thanks for reply. >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> I have started exploring SVD based on its >> > > mention >> > > > > of >> > > > > > > > could >> > > > > > > > >>> help >> > > > > > > > >>> > > to >> > > > > > > > >>> > > > > drop >> > > > > > > > >>> > > > > > >>> features which are not relevant for >> > clustering. >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> My objective is reduce number of features >> > > before >> > > > > > passing >> > > > > > > > >>> them >> > > > > > > > >>> > to >> > > > > > > > >>> > > > > > >>> clustering >> > > > > > > > >>> > > > > > >>> and just keep important features. >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> arff/csv==> ssvd (for dimensionality >> > reduction) >> > > > ==> >> > > > > > > > >>> clustering >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> Could you please illustrate mahout props >> to >> > > join >> > > > > > above >> > > > > > > > >>> > pipeline. >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> I think, Lanczos SVD needs to be used for >> mxm >> > > > > matrix. >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> I have tried check ssvd, I have used >> > > arff.vector >> > > > to >> > > > > > > > covert >> > > > > > > > >>> > > arff/csv >> > > > > > > > >>> > > > > to >> > > > > > > > >>> > > > > > >>> vector file which is then give as input to >> > ssvd >> > > > and >> > > > > > them >> > > > > > > > >>> dumped >> > > > > > > > >>> > > U, >> > > > > > > > >>> > > > V >> > > > > > > > >>> > > > > > and >> > > > > > > > >>> > > > > > >>> sigma using vectordump. >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> I see most of the values dumped are near >> to >> > 0. >> > > I >> > > > > dont >> > > > > > > > >>> > understand >> > > > > > > > >>> > > is >> > > > > > > > >>> > > > > > this >> > > > > > > > >>> > > > > > >>> correct or not. >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > >> > > > > > > > >>> > > >> > > > > > > > >>> > >> > > > > > > > >>> >> > > > > > > > >> > > > > > >> > > > > >> > > > >> > > >> > >> {0:0.01066724825049657,1:0.016715498597386844,2:2.0187750952311708E-4,3:3.401020567221039E-4,4:-1.2388403347280688E-4,5:6.41502463540719E-5,6:-1.359187582538833E-4,7:6.329813140445419E-5,8:1.670015585746444E-4,9:3.5415113034592744E-4,10:7.108868213280763E-4,11:0.020553517552052456,12:-0.015118680942548916,13:0.007981746711271956,14:-0.003251236468768259,15:0.0038075014396303053,16:-0.0010925318534013683,17:-0.0026943024876179833,18:-0.001744794617721648,19:-0.0024528466548735714} >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > >> > > > > > > > >>> > > >> > > > > > > > >>> > >> > > > > > > > >>> >> > > > > > > > >> > > > > > >> > > > > >> > > > >> > > >> > >> {0:0.029978614322360833,1:-0.01431521245087889,2:1.3318592088199427E-4,3:1.495356283071516E-4,4:8.762709213918985E-5,5:1.2765191352425177E- >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> Thanks, >> > > > > > > > >>> > > > > > >>> Rajesh >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> On Tue, May 21, 2013 at 11:35 AM, Ted >> > Dunning < >> > > > > > > > >>> > > > ted.dunn...@gmail.com >> > > > > > > > >>> > > > > > >> > > > > > > > >>> > > > > > >>> wrote: >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >>> > Are you using Lanczos instead of SSVD >> for a >> > > > > reason? >> > > > > > > > >>> > > > > > >>> > >> > > > > > > > >>> > > > > > >>> > >> > > > > > > > >>> > > > > > >>> > >> > > > > > > > >>> > > > > > >>> > >> > > > > > > > >>> > > > > > >>> > On Mon, May 20, 2013 at 4:13 AM, Rajesh >> > > Nikam < >> > > > > > > > >>> > > > > rajeshni...@gmail.com >> > > > > > > > >>> > > > > > > >> > > > > > > > >>> > > > > > >>> > wrote: >> > > > > > > > >>> > > > > > >>> > >> > > > > > > > >>> > > > > > >>> > > Hello, >> > > > > > > > >>> > > > > > >>> > > >> > > > > > > > >>> > > > > > >>> > > I have arff / csv file containing >> input >> > > data >> > > > > > that I >> > > > > > > > >>> want to >> > > > > > > > >>> > > > pass >> > > > > > > > >>> > > > > to >> > > > > > > > >>> > > > > > >>> svd : >> > > > > > > > >>> > > > > > >>> > > Lanczos Singular Value Decomposition. >> > > > > > > > >>> > > > > > >>> > > >> > > > > > > > >>> > > > > > >>> > > Which tool to use to convert it to >> > required >> > > > > > format ? >> > > > > > > > >>> > > > > > >>> > > >> > > > > > > > >>> > > > > > >>> > > Thanks in Advance ! >> > > > > > > > >>> > > > > > >>> > > >> > > > > > > > >>> > > > > > >>> > > Thanks, >> > > > > > > > >>> > > > > > >>> > > Rajesh >> > > > > > > > >>> > > > > > >>> > > >> > > > > > > > >>> > > > > > >>> > >> > > > > > > > >>> > > > > > >>> >> > > > > > > > >>> > > > > > >> >> > > > > > > > >>> > > > > > > >> > > > > > > > >>> > > > > > >> > > > > > > > >>> > > > > >> > > > > > > > >>> > > > >> > > > > > > > >>> > > >> > > > > > > > >>> > >> > > > > > > > >>> >> > > > > > > > >> >> > > > > > > > >> >> > > > > > > > > >> > > > > > > > >> > > > > > >> > > > >> > > >> > >> >
