You should be using 'pca' with ssvd mahout ssvd -i /user/hadoop/t/input-set-vector/ -o /user/hadoop/t/input-set-svd/ -k 50 --reduceTasks 2 -U true -V false -us true -ow -pca true
You should be using USigma (U*Sigma) this is generated by the 'us' option. ________________________________ From: Rajesh Nikam <[email protected]> To: [email protected]; Suneel Marthi <[email protected]> Sent: Thursday, May 30, 2013 8:44 AM Subject: Re: Fwd: Re: convert input for SVD 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 <[email protected]> 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 <[email protected]> >To: [email protected] >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 <[email protected]>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 <[email protected]> >> 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" <[email protected]> >> 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 <[email protected]> >> > > To: [email protected] >> > > 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" <[email protected]> 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" <[email protected]> >> > > > Date: May 24, 2013 3:20 AM >> > > > Subject: Re: convert input for SVD >> > > > To: <[email protected]> >> > > > 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 < >> [email protected] >> > > >> > > > 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 §3 for details" >> > > > > > >> > > > > > >> > > > > > On Thu, May 23, 2013 at 6:24 PM, Dmitriy Lyubimov < >> > [email protected] >> > > > >> > > > > > 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 < >> > > [email protected] >> > > > > > >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 < >> > > > [email protected] >> > > > > > >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" < >> [email protected] >> > > >> > > > 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 < >> > > > [email protected] >> > > > > > > >> > > > > > >>> > 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" < >> > > [email protected] >> > > > > >> > > > > > >>> 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 < >> > > > > > >>> [email protected]> >> > > > > > >>> > > > 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 < >> > > > > > >>> [email protected] >> > > > > > >>> > > >> > > > > > >>> > > > > 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 < >> > > > > > >>> > [email protected] >> > > > > > >>> > > > >> > > > > > >>> > > > > > 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 < >> > > > > > >>> > > [email protected] >> > > > > > >>> > > > > > >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" < >> > > > > > >>> [email protected]> >> > > > > > >>> > > > > 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 < >> > > > > > >>> > > > [email protected] >> > > > > > >>> > > > > > >> > > > > > >>> > > > > > >>> wrote: >> > > > > > >>> > > > > > >>> >> > > > > > >>> > > > > > >>> > Are you using Lanczos instead of SSVD for a >> > > reason? >> > > > > > >>> > > > > > >>> > >> > > > > > >>> > > > > > >>> > >> > > > > > >>> > > > > > >>> > >> > > > > > >>> > > > > > >>> > >> > > > > > >>> > > > > > >>> > On Mon, May 20, 2013 at 4:13 AM, Rajesh >> Nikam < >> > > > > > >>> > > > > [email protected] >> > > > > > >>> > > > > > > >> > > > > > >>> > > > > > >>> > 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 >> > > > > > >>> > > > > > >>> > > >> > > > > > >>> > > > > > >>> > >> > > > > > >>> > > > > > >>> >> > > > > > >>> > > > > > >> >> > > > > > >>> > > > > > > >> > > > > > >>> > > > > > >> > > > > > >>> > > > > >> > > > > > >>> > > > >> > > > > > >>> > > >> > > > > > >>> > >> > > > > > >>> >> > > > > > >> >> > > > > > >> >> > > > > > > >> > > > > > >> > > > >> > >>
