Got it- in short no. Think of the keys like a dictionary or HashMap.
That's why everything is ending up on row 1. What are you trying to achieve by creating keys of 1? Trevor Grant Data Scientist https://github.com/rawkintrevo http://stackexchange.com/users/3002022/rawkintrevo http://trevorgrant.org *"Fortunate is he, who is able to know the causes of things." -Virgil* On Fri, Apr 21, 2017 at 2:26 PM, KHATWANI PARTH BHARAT < h2016...@pilani.bits-pilani.ac.in> wrote: > @Trevor > > > > In was trying to write the "*Kmeans*" Using Mahout DRM as per the algorithm > outlined by Dmitriy. > I was facing the Problem of assigning cluster Ids to the Row Keys > For Example > Consider the below matrix Where column 1 to 3 are the data points and > column 0 Containing the count of the point > { > 0 => {0:1.0, 1: 1.0, 2: 1.0, 3: 3.0} > 1 => {0:1.0, 1: 2.0, 2: 3.0, 3: 4.0} > 2 => {0:1.0, 1: 3.0, 2: 4.0, 3: 5.0} > 3 => {0:1.0, 1: 4.0, 2: 5.0, 3: 6.0} > } > > now after calculating the centriod which closest to the data point data > zeroth index i am trying to assign the centriod index to *row key * > > Now Suppose say that every data point is assigned to centriod at index 1 > so after assigning the key=1 to each and every row > > using the code below > > val drm2 = A.mapBlock() { > case (keys, block) => for(row <- 0 until keys.size) { > > * //assigning 1 to each row index* keys(row) = 1 > } (keys, block) } > > > > I want above matrix to be in this form > > > { > 1 => {0:1.0, 1: 1.0, 2: 1.0, 3: 3.0} > 1 => {0:1.0, 1: 2.0, 2: 3.0, 3: 4.0} > 1 => {0:1.0, 1: 3.0, 2: 4.0, 3: 5.0} > 1 => {0:1.0, 1: 4.0, 2: 5.0, 3: 6.0} > } > > > > > Turns out to be this > { > 0 => {} > 1 => {0:1.0,1:4.0,2:5.0,3:6.0} > 2 => {} > 3 => {} > } > > > > I am confused weather assigning the new Key Values to the row index is done > through the following code line > > * //assigning 1 to each row index* keys(row) = 1 > > > or is there any other way. > > > > I am not able to find any use links or reference on internet even Andrew > and Dmitriy's book also does not have any proper reference for the > above mentioned issue. > > > > Thanks & Regards > Parth Khatwani > > > > On Fri, Apr 21, 2017 at 10:06 PM, Trevor Grant <trevor.d.gr...@gmail.com> > wrote: > > > OK, i dug into this before i read your question carefully, that was my > bad. > > > > Assuming you want the aggregate transpose of : > > { > > 0 => {0:1.0, 1: 1.0, 2: 1.0, 3: 3.0} > > 1 => {0:1.0, 1: 2.0, 2: 3.0, 3: 4.0} > > 2 => {0:1.0, 1: 3.0, 2: 4.0, 3: 5.0} > > 3 => {0:1.0, 1: 4.0, 2: 5.0, 3: 6.0} > > } > > > > to be > > { > > 0 => {1: 5.0} // (not 4.0) // and 6.0 in your example... > > 1 => {1: 9.0} > > 2 => {1: 12.0} > > 3 => {1: 15.0} > > } > > > > > > Then why not replace the mapBlock statement as follows: > > > > val drm2 = (A(::, 1 until 4) cbind 0.0).mapBlock() { > > case (keys, block) => > > for(row <- 0 until block.nrow) block(row, 3) = block(row, ::).sum > > (keys, block) > > } > > val aggTranspose = drm2(::, 3 until 4).t > > println("Result of aggregating tranpose") > > println(""+aggTranspose.collect) > > > > Where we are creating an empty row, then filling it with the row sums. > > > > A distributed rowSums fn would be nice for just such an occasion... sigh > > > > Let me know if that gets you going again. That was simpler than I > thought- > > sorry for delay on this. > > > > PS > > Candidly, I didn't explore further once i understood teh question, but if > > you are going to collect this to the driver anyway (not sure if that is > the > > case) > > A(::, 1 until 4).rowSums would also work. > > > > > > > > > > > > Trevor Grant > > Data Scientist > > https://github.com/rawkintrevo > > http://stackexchange.com/users/3002022/rawkintrevo > > http://trevorgrant.org > > > > *"Fortunate is he, who is able to know the causes of things." -Virgil* > > > > > > On Thu, Apr 20, 2017 at 9:01 PM, KHATWANI PARTH BHARAT < > > h2016...@pilani.bits-pilani.ac.in> wrote: > > > > > @Trevor Sir, > > > I have attached the sample data file and here is the line to complete > > the Data > > > File <https://drive.google.com/open?id=0Bxnnu_Ig2Et9QjZoM3dmY1V5WXM>. > > > > > > > > > Following is the link for the Github Branch For the code > > > https://github.com/parth2691/Spark_Mahout/tree/Dmitriy-Lyubimov > > > > > > KmeansMahout.scala > > > <https://github.com/parth2691/Spark_Mahout/blob/Dmitriy- > > Lyubimov/KmeansMahout.scala> is > > > the complete code > > > > > > > > > I also have made sample program just to test the assigning new values > to > > > the key to Row Matrix and aggregating transpose.I think assigning new > > > values to the key to Row Matrix and aggregating transpose is causing > the > > > main problem in Kmean code > > > Following is the link to Github repo for this code. > > > TestClusterAssign.scala > > > <https://github.com/parth2691/Spark_Mahout/blob/Dmitriy- > > Lyubimov/TestClusterAssign.scala> > > > > > > above code contains the hard coded data. Following is the expected and > > the > > > actual output of the above code > > > Out of 1st println After New Cluster assignment should be > > > This > > > { > > > 0 => {0:1.0, 1: 1.0, 2: 1.0, 3: 3.0} > > > 1 => {0:1.0, 1: 2.0, 2: 3.0, 3: 4.0} > > > 2 => {0:1.0, 1: 3.0, 2: 4.0, 3: 5.0} > > > 3 => {0:1.0, 1: 4.0, 2: 5.0, 3: 6.0} > > > } > > > (Here zeroth Column is used to store the centriod count and column 1,2 > > and > > > 3 Contains Data) > > > > > > But Turns out to be this > > > { > > > 0 => {} > > > 1 => {0:1.0,1:4.0,2:5.0,3:6.0} > > > 2 => {} > > > 3 => {} > > > } > > > And the result of aggregating Transpose should be > > > { > > > 0 => {1: 4.0} > > > 1 => {1: 9.0} > > > 2 => {1: 12.0} > > > 3 => {1: 15.0} > > > } > > > > > > > > > Thanks Trevor for such a great Help > > > > > > > > > > > > > > > Best Regards > > > Parth > > > > > > > > > > > > > > > > > > > > > > > > > > > On Fri, Apr 21, 2017 at 4:20 AM, Trevor Grant < > trevor.d.gr...@gmail.com> > > > wrote: > > > > > >> Hey > > >> > > >> Sorry for delay- was getting ready to tear into this. > > >> > > >> Would you mind posting a small sample of data that you would expect > this > > >> application to consume. > > >> > > >> tg > > >> > > >> > > >> Trevor Grant > > >> Data Scientist > > >> https://github.com/rawkintrevo > > >> http://stackexchange.com/users/3002022/rawkintrevo > > >> http://trevorgrant.org > > >> > > >> *"Fortunate is he, who is able to know the causes of things." > -Virgil* > > >> > > >> > > >> On Tue, Apr 18, 2017 at 11:32 PM, KHATWANI PARTH BHARAT < > > >> h2016...@pilani.bits-pilani.ac.in> wrote: > > >> > > >> > @Dmitriy,@Trevor and @Andrew Sir, > > >> > I am still stuck at the above problem can you please help me out > with > > >> it. > > >> > I am unable to find the proper reference to solve the above issue. > > >> > > > >> > Thanks & Regards > > >> > Parth Khatwani > > >> > > > >> > > > >> > > > >> > > > >> > > > >> > > > >> > > > >> > > > >> > <https://mailtrack.io/> Sent with Mailtrack > > >> > <https://mailtrack.io/install?source=signature&lang=en&; > > >> > referral=h2016...@pilani.bits-pilani.ac.in&idSignature=22> > > >> > > > >> > On Sat, Apr 15, 2017 at 10:07 AM, KHATWANI PARTH BHARAT < > > >> > h2016...@pilani.bits-pilani.ac.in> wrote: > > >> > > > >> > > @Dmitriy, > > >> > > @Trevor and @Andrew > > >> > > > > >> > > I have tried > > >> > > Testing this Row Key assignment issue which i have mentioned in > the > > >> above > > >> > > mail, > > >> > > By Writing the a separate code where i am assigning the a default > > >> value 1 > > >> > > to each row Key of The DRM and then taking the aggregating > transpose > > >> > > I have committed the separate test code to the Github Branch > > >> > > <https://github.com/parth2691/Spark_Mahout/tree/Dmitriy-Lyubimov > >. > > >> > > > > >> > > The Code is as follows > > >> > > > > >> > > val inCoreA = dense((1,1, 2, 3), (1,2, 3, 4), (1,3, 4, 5), (1,4, > 5, > > >> 6)) > > >> > > val A = drmParallelize(m = inCoreA) > > >> > > > > >> > > //Mapblock > > >> > > val drm2 = A.mapBlock() { > > >> > > case (keys, block) => for(row <- 0 until keys.size) { > > >> > > > > >> > > * //assigning 1 to each row index* keys(row) = 1 > > >> > } (keys, block) } prinln("After New Cluster > > assignment") > > >> > println(""+drm2.collect) val aggTranspose = drm2.t > > >> println("Result of > > >> > aggregating tranpose") println(""+aggTranspose.collect) > > >> > > > > >> > > Out of 1st println After New Cluster assignment should be > > >> > > This > > >> > > { > > >> > > 0 => {0:1.0, 1: 1.0, 2: 1.0, 3: 3.0} > > >> > > 1 => {0:1.0, 1: 2.0, 2: 3.0, 3: 4.0} > > >> > > 2 => {0:1.0, 1: 3.0, 2: 4.0, 3: 5.0} > > >> > > 3 => {0:1.0, 1: 4.0, 2: 5.0, 3: 6.0} > > >> > > } > > >> > > (Here zeroth Column is used to store the centriod count and column > > 1,2 > > >> > and > > >> > > 3 Contains Data) > > >> > > > > >> > > But Turns out to be this > > >> > > { > > >> > > 0 => {} > > >> > > 1 => {0:1.0,1:4.0,2:5.0,3:6.0} > > >> > > 2 => {} > > >> > > 3 => {} > > >> > > } > > >> > > And the result of aggregating Transpose should be > > >> > > { > > >> > > 0 => {1: 4.0} > > >> > > 1 => {1: 9.0} > > >> > > 2 => {1: 12.0} > > >> > > 3 => {1: 15.0} > > >> > > } > > >> > > > > >> > > > > >> > > I have referred to the book written by Andrew And Dmitriy Apache > > >> Mahout: > > >> > > Beyond MapReduce > > >> > > <https://www.amazon.com/Apache-Mahout-MapReduce- > > >> > Dmitriy-Lyubimov/dp/1523775785> Aggregating > > >> > > Transpose and other concepts are explained very nicely over here > > but > > >> i > > >> > am > > >> > > unable to find any example where > > >> > > Row Keys are assigned new Values . Mahout Samsara Manual > > >> > > http://apache.github.io/mahout/doc/ScalaSparkBindings.html Also > > Does > > >> not > > >> > > contain any such examples. > > >> > > It will great if i can get some reference to solution of mentioned > > >> issue. > > >> > > > > >> > > > > >> > > Thanks > > >> > > Parth Khatwani > > >> > > > > >> > > > > >> > > > > >> > > On Sat, Apr 15, 2017 at 12:13 AM, Andrew Palumbo < > > ap....@outlook.com> > > >> > > wrote: > > >> > > > > >> > >> +1 > > >> > >> > > >> > >> > > >> > >> > > >> > >> Sent from my Verizon Wireless 4G LTE smartphone > > >> > >> > > >> > >> > > >> > >> -------- Original message -------- > > >> > >> From: Trevor Grant <trevor.d.gr...@gmail.com> > > >> > >> Date: 04/14/2017 11:40 (GMT-08:00) > > >> > >> To: dev@mahout.apache.org > > >> > >> Subject: Re: Trying to write the KMeans Clustering Using "Apache > > >> Mahout > > >> > >> Samsara" > > >> > >> > > >> > >> Parth and Dmitriy, > > >> > >> > > >> > >> This is awesome- as a follow on can we work on getting this > rolled > > >> in to > > >> > >> the algorithms framework? > > >> > >> > > >> > >> Happy to work with you on this Parth! > > >> > >> > > >> > >> Trevor Grant > > >> > >> Data Scientist > > >> > >> https://github.com/rawkintrevo > > >> > >> http://stackexchange.com/users/3002022/rawkintrevo > > >> > >> http://trevorgrant.org > > >> > >> > > >> > >> *"Fortunate is he, who is able to know the causes of things." > > >> -Virgil* > > >> > >> > > >> > >> > > >> > >> On Fri, Apr 14, 2017 at 1:27 PM, Dmitriy Lyubimov < > > dlie...@gmail.com > > >> > > > >> > >> wrote: > > >> > >> > > >> > >> > i would think reassinging keys should work in most cases. > > >> > >> > The only exception is that technically Spark contracts imply > that > > >> > effect > > >> > >> > should be idempotent if task is retried, which might be a > problem > > >> in a > > >> > >> > specific scenario of the object tree coming out from block > cache > > >> > object > > >> > >> > tree, which can stay there and be retried again. but > specifically > > >> > w.r.t. > > >> > >> > this key assignment i don't see any problem since the action > > >> obviously > > >> > >> > would be idempotent even if this code is run multiple times on > > the > > >> > same > > >> > >> > (key, block) pair. This part should be good IMO. > > >> > >> > > > >> > >> > On Fri, Apr 14, 2017 at 2:26 AM, KHATWANI PARTH BHARAT < > > >> > >> > h2016...@pilani.bits-pilani.ac.in> wrote: > > >> > >> > > > >> > >> > > @Dmitriy Sir, > > >> > >> > > In the K means code above I think i am doing the following > > >> > Incorrectly > > >> > >> > > > > >> > >> > > Assigning the closest centriod index to the Row Keys of DRM > > >> > >> > > > > >> > >> > > //11. Iterating over the Data Matrix(in DrmLike[Int] format) > to > > >> > >> calculate > > >> > >> > > the initial centriods > > >> > >> > > dataDrmX.mapBlock() { > > >> > >> > > case (keys, block) => > > >> > >> > > for (row <- 0 until block.nrow) { > > >> > >> > > var dataPoint = block(row, ::) > > >> > >> > > > > >> > >> > > //12. findTheClosestCentriod find the closest > > centriod > > >> to > > >> > >> the > > >> > >> > > Data point specified by "dataPoint" > > >> > >> > > val closesetIndex = findTheClosestCentriod( > > dataPoint, > > >> > >> > centriods) > > >> > >> > > > > >> > >> > > //13. assigning closest index to key > > >> > >> > > keys(row) = closesetIndex > > >> > >> > > } > > >> > >> > > keys -> block > > >> > >> > > } > > >> > >> > > > > >> > >> > > in step 12 i am finding the centriod closest to the current > > >> > dataPoint > > >> > >> > > in step13 i am assigning the closesetIndex to the key of the > > >> > >> > corresponding > > >> > >> > > row represented by the dataPoint > > >> > >> > > I think i am doing step13 incorrectly. > > >> > >> > > > > >> > >> > > Also i am unable to find the proper reference for the same in > > the > > >> > >> > reference > > >> > >> > > links which you have mentioned above > > >> > >> > > > > >> > >> > > > > >> > >> > > Thanks & Regards > > >> > >> > > Parth Khatwani > > >> > >> > > > > >> > >> > > > > >> > >> > > > > >> > >> > > > > >> > >> > > > > >> > >> > > On Thu, Apr 13, 2017 at 6:24 PM, KHATWANI PARTH BHARAT < > > >> > >> > > h2016...@pilani.bits-pilani.ac.in> wrote: > > >> > >> > > > > >> > >> > > > Dmitriy Sir, > > >> > >> > > > I have Created a github branch Github Branch Having Initial > > >> Kmeans > > >> > >> Code > > >> > >> > > > <https://github.com/parth2691/ > Spark_Mahout/tree/Dmitriy-Lyub > > >> imov> > > >> > >> > > > > > >> > >> > > > > > >> > >> > > > Thanks & Regards > > >> > >> > > > Parth Khatwani > > >> > >> > > > > > >> > >> > > > On Thu, Apr 13, 2017 at 3:19 AM, Andrew Palumbo < > > >> > ap....@outlook.com > > >> > >> > > > >> > >> > > > wrote: > > >> > >> > > > > > >> > >> > > >> +1 to creating a branch. > > >> > >> > > >> > > >> > >> > > >> > > >> > >> > > >> > > >> > >> > > >> Sent from my Verizon Wireless 4G LTE smartphone > > >> > >> > > >> > > >> > >> > > >> > > >> > >> > > >> -------- Original message -------- > > >> > >> > > >> From: Dmitriy Lyubimov <dlie...@gmail.com> > > >> > >> > > >> Date: 04/12/2017 11:25 (GMT-08:00) > > >> > >> > > >> To: dev@mahout.apache.org > > >> > >> > > >> Subject: Re: Trying to write the KMeans Clustering Using > > >> "Apache > > >> > >> > Mahout > > >> > >> > > >> Samsara" > > >> > >> > > >> > > >> > >> > > >> can't say i can read this code well formatted that way... > > >> > >> > > >> > > >> > >> > > >> it would seem to me that the code is not using the > broadcast > > >> > >> variable > > >> > >> > > and > > >> > >> > > >> instead is using closure variable. that's the only thing i > > can > > >> > >> > > immediately > > >> > >> > > >> see by looking in the middle of it. > > >> > >> > > >> > > >> > >> > > >> it would be better if you created a branch on github for > > that > > >> > code > > >> > >> > that > > >> > >> > > >> would allow for easy check-outs and comments. > > >> > >> > > >> > > >> > >> > > >> -d > > >> > >> > > >> > > >> > >> > > >> On Wed, Apr 12, 2017 at 10:29 AM, KHATWANI PARTH BHARAT < > > >> > >> > > >> h2016...@pilani.bits-pilani.ac.in> wrote: > > >> > >> > > >> > > >> > >> > > >> > @Dmitriy Sir > > >> > >> > > >> > > > >> > >> > > >> > I have completed the Kmeans code as per the algorithm > you > > >> have > > >> > >> > Outline > > >> > >> > > >> > above > > >> > >> > > >> > > > >> > >> > > >> > My code is as follows > > >> > >> > > >> > > > >> > >> > > >> > This code works fine till step number 10 > > >> > >> > > >> > > > >> > >> > > >> > In step 11 i am assigning the new centriod index to > > >> > >> corresponding > > >> > >> > row > > >> > >> > > >> key > > >> > >> > > >> > of data Point in the matrix > > >> > >> > > >> > I think i am doing something wrong in step 11 may be i > am > > >> using > > >> > >> > > >> incorrect > > >> > >> > > >> > syntax > > >> > >> > > >> > > > >> > >> > > >> > Can you help me find out what am i doing wrong. > > >> > >> > > >> > > > >> > >> > > >> > > > >> > >> > > >> > //start of main method > > >> > >> > > >> > > > >> > >> > > >> > def main(args: Array[String]) { > > >> > >> > > >> > //1. initialize the spark and mahout context > > >> > >> > > >> > val conf = new SparkConf() > > >> > >> > > >> > .setAppName("DRMExample") > > >> > >> > > >> > .setMaster(args(0)) > > >> > >> > > >> > .set("spark.serializer", > > "org.apache.spark.serializer. > > >> > >> > > >> > KryoSerializer") > > >> > >> > > >> > .set("spark.kryo.registrator", > > >> > >> > > >> > "org.apache.mahout.sparkbindings.io. > > MahoutKryoRegistrator") > > >> > >> > > >> > implicit val sc = new SparkDistributedContext(new > > >> > >> > > >> SparkContext(conf)) > > >> > >> > > >> > > > >> > >> > > >> > //2. read the data file and save it in the rdd > > >> > >> > > >> > val lines = sc.textFile(args(1)) > > >> > >> > > >> > > > >> > >> > > >> > //3. convert data read in as string in to array of > > >> double > > >> > >> > > >> > val test = lines.map(line => > > >> line.split('\t').map(_.toDoubl > > >> > >> e)) > > >> > >> > > >> > > > >> > >> > > >> > //4. add a column having value 1 in array of double > > this > > >> > will > > >> > >> > > >> > create something like (1 | D)', which will be used > while > > >> > >> > calculating > > >> > >> > > >> > (1 | D)' > > >> > >> > > >> > val augumentedArray = test.map(addCentriodColumn _) > > >> > >> > > >> > > > >> > >> > > >> > //5. convert rdd of array of double in rdd of > > >> DenseVector > > >> > >> > > >> > val rdd = augumentedArray.map(dvec(_)) > > >> > >> > > >> > > > >> > >> > > >> > //6. convert rdd to DrmRdd > > >> > >> > > >> > val rddMatrixLike: DrmRdd[Int] = > rdd.zipWithIndex.map > > { > > >> > case > > >> > >> (v, > > >> > >> > > >> > idx) => (idx.toInt, v) } //7. convert DrmRdd to > > >> > >> > > >> > CheckpointedDrm[Int] val matrix = > > drmWrap(rddMatrixLike) > > >> > >> //8. > > >> > >> > > >> > seperating the column having all ones created in step 4 > > and > > >> > will > > >> > >> use > > >> > >> > > >> > it later val oneVector = matrix(::, 0 until 1) > > >> //9. > > >> > >> final > > >> > >> > > >> > input data in DrmLike[Int] format val dataDrmX = > > >> matrix(::, > > >> > 1 > > >> > >> > until > > >> > >> > > >> > 4) //9. Sampling to select initial centriods > > >> val > > >> > >> > > >> > centriods = drmSampleKRows(dataDrmX, 2, false) > > >> > centriods.size > > >> > >> > > >> > //10. Broad Casting the initial centriods val > > >> > broadCastMatrix > > >> > >> = > > >> > >> > > >> > drmBroadcast(centriods) //11. Iterating over > > the > > >> > Data > > >> > >> > > >> > Matrix(in DrmLike[Int] format) to calculate the initial > > >> > centriods > > >> > >> > > >> > dataDrmX.mapBlock() { case (keys, block) => > > for > > >> > (row > > >> > >> <- > > >> > >> > 0 > > >> > >> > > >> > until block.nrow) { var dataPoint = block(row, > > ::) > > >> > >> > > >> > //12. findTheClosestCentriod find the closest > > >> centriod > > >> > to > > >> > >> > the > > >> > >> > > >> > Data point specified by "dataPoint" val > > >> closesetIndex > > >> > = > > >> > >> > > >> > findTheClosestCentriod(dataPoint, centriods) > > >> > >> > //13. > > >> > >> > > >> > assigning closest index to key keys(row) = > > >> > closesetIndex > > >> > >> > > >> > } keys -> block } > > >> > >> > > >> > > > >> > >> > > >> > //14. Calculating the (1|D) val b = (oneVector > > >> cbind > > >> > >> > > >> > dataDrmX) //15. Aggregating Transpose (1|D)' > val > > >> > >> > bTranspose > > >> > >> > > >> > = (oneVector cbind dataDrmX).t // after step 15 > > >> bTranspose > > >> > >> will > > >> > >> > > >> > have data in the following format /*(n+1)*K where > > >> > >> n=dimension > > >> > >> > > >> > of the data point, K=number of clusters * zeroth row > > will > > >> > >> contain > > >> > >> > > >> > the count of points assigned to each cluster * > assuming > > >> 3d > > >> > >> data > > >> > >> > > >> > points * */ > > >> > >> > > >> > > > >> > >> > > >> > > > >> > >> > > >> > val nrows = b.nrow.toInt //16. slicing the count > > >> vectors > > >> > >> out > > >> > >> > > >> > val pointCountVectors = drmBroadcast(b(0 until 1, > > >> > ::).collect(0, > > >> > >> > ::)) > > >> > >> > > >> > val vectorSums = b(1 until nrows, ::) //17. > dividing > > >> the > > >> > >> data > > >> > >> > > >> > point by count vector vectorSums.mapBlock() { > case > > >> > (keys, > > >> > >> > > >> > block) => for (row <- 0 until block.nrow) { > > >> > >> > block(row, > > >> > >> > > >> > ::) /= pointCountVectors } keys -> block > > } > > >> > >> //18. > > >> > >> > > >> > seperating the count vectors val newCentriods = > > >> > >> vectorSums.t(::,1 > > >> > >> > > >> > until centriods.size) //19. iterate over the > > >> above > > >> > >> code > > >> > >> > > >> > till convergence criteria is meet }//end of main > method > > >> > >> > > >> > > > >> > >> > > >> > > > >> > >> > > >> > > > >> > >> > > >> > // method to find the closest centriod to data point( > > vec: > > >> > >> Vector > > >> > >> > > >> > in the arguments) def findTheClosestCentriod(vec: > Vector, > > >> > >> matrix: > > >> > >> > > >> > Matrix): Int = { > > >> > >> > > >> > var index = 0 > > >> > >> > > >> > var closest = Double.PositiveInfinity > > >> > >> > > >> > for (row <- 0 until matrix.nrow) { > > >> > >> > > >> > val squaredSum = ssr(vec, matrix(row, ::)) > > >> > >> > > >> > val tempDist = Math.sqrt(ssr(vec, matrix(row, > ::))) > > >> > >> > > >> > if (tempDist < closest) { > > >> > >> > > >> > closest = tempDist > > >> > >> > > >> > index = row > > >> > >> > > >> > } > > >> > >> > > >> > } > > >> > >> > > >> > index > > >> > >> > > >> > } > > >> > >> > > >> > > > >> > >> > > >> > //calculating the sum of squared distance between the > > >> > >> > > points(Vectors) > > >> > >> > > >> > def ssr(a: Vector, b: Vector): Double = { > > >> > >> > > >> > (a - b) ^= 2 sum > > >> > >> > > >> > } > > >> > >> > > >> > > > >> > >> > > >> > //method used to create (1|D) > > >> > >> > > >> > def addCentriodColumn(arg: Array[Double]): > Array[Double] > > >> = { > > >> > >> > > >> > val newArr = new Array[Double](arg.length + 1) > > >> > >> > > >> > newArr(0) = 1.0; > > >> > >> > > >> > for (i <- 0 until (arg.size)) { > > >> > >> > > >> > newArr(i + 1) = arg(i); > > >> > >> > > >> > } > > >> > >> > > >> > newArr > > >> > >> > > >> > } > > >> > >> > > >> > > > >> > >> > > >> > > > >> > >> > > >> > Thanks & Regards > > >> > >> > > >> > Parth Khatwani > > >> > >> > > >> > > > >> > >> > > >> > > > >> > >> > > >> > > > >> > >> > > >> > On Mon, Apr 3, 2017 at 7:37 PM, KHATWANI PARTH BHARAT < > > >> > >> > > >> > h2016...@pilani.bits-pilani.ac.in> wrote: > > >> > >> > > >> > > > >> > >> > > >> > > > > >> > >> > > >> > > ---------- Forwarded message ---------- > > >> > >> > > >> > > From: Dmitriy Lyubimov <dlie...@gmail.com> > > >> > >> > > >> > > Date: Fri, Mar 31, 2017 at 11:34 PM > > >> > >> > > >> > > Subject: Re: Trying to write the KMeans Clustering > Using > > >> > >> "Apache > > >> > >> > > >> Mahout > > >> > >> > > >> > > Samsara" > > >> > >> > > >> > > To: "dev@mahout.apache.org" <dev@mahout.apache.org> > > >> > >> > > >> > > > > >> > >> > > >> > > > > >> > >> > > >> > > ps1 this assumes row-wise construction of A based on > > >> training > > >> > >> set > > >> > >> > > of m > > >> > >> > > >> > > n-dimensional points. > > >> > >> > > >> > > ps2 since we are doing multiple passes over A it may > > make > > >> > >> sense to > > >> > >> > > >> make > > >> > >> > > >> > > sure it is committed to spark cache (by using > checkpoint > > >> > api), > > >> > >> if > > >> > >> > > >> spark > > >> > >> > > >> > is > > >> > >> > > >> > > used > > >> > >> > > >> > > > > >> > >> > > >> > > On Fri, Mar 31, 2017 at 10:53 AM, Dmitriy Lyubimov < > > >> > >> > > dlie...@gmail.com > > >> > >> > > >> > > > >> > >> > > >> > > wrote: > > >> > >> > > >> > > > > >> > >> > > >> > > > here is the outline. For details of APIs, please > refer > > >> to > > >> > >> > samsara > > >> > >> > > >> > manual > > >> > >> > > >> > > > [2], i will not be be repeating it. > > >> > >> > > >> > > > > > >> > >> > > >> > > > Assume your training data input is m x n matrix A. > For > > >> > >> > simplicity > > >> > >> > > >> let's > > >> > >> > > >> > > > assume it's a DRM with int row keys, i.e., > > DrmLike[Int]. > > >> > >> > > >> > > > > > >> > >> > > >> > > > Initialization: > > >> > >> > > >> > > > > > >> > >> > > >> > > > First, classic k-means starts by selecting initial > > >> > clusters, > > >> > >> by > > >> > >> > > >> > sampling > > >> > >> > > >> > > > them out. You can do that by using sampling api [1], > > >> thus > > >> > >> > forming > > >> > >> > > a > > >> > >> > > >> k > > >> > >> > > >> > x n > > >> > >> > > >> > > > in-memory matrix C (current centroids). C is > therefore > > >> of > > >> > >> > Mahout's > > >> > >> > > >> > Matrix > > >> > >> > > >> > > > type. > > >> > >> > > >> > > > > > >> > >> > > >> > > > You the proceed by alternating between cluster > > >> assignments > > >> > >> and > > >> > >> > > >> > > > recompupting centroid matrix C till convergence > based > > on > > >> > some > > >> > >> > test > > >> > >> > > >> or > > >> > >> > > >> > > > simply limited by epoch count budget, your choice. > > >> > >> > > >> > > > > > >> > >> > > >> > > > Cluster assignments: here, we go over current > > generation > > >> > of A > > >> > >> > and > > >> > >> > > >> > > > recompute centroid indexes for each row in A. Once > we > > >> > >> recompute > > >> > >> > > >> index, > > >> > >> > > >> > we > > >> > >> > > >> > > > put it into the row key . You can do that by > assigning > > >> > >> centroid > > >> > >> > > >> indices > > >> > >> > > >> > > to > > >> > >> > > >> > > > keys of A using operator mapblock() (details in [2], > > >> [3], > > >> > >> [4]). > > >> > >> > > You > > >> > >> > > >> > also > > >> > >> > > >> > > > need to broadcast C in order to be able to access it > > in > > >> > >> > efficient > > >> > >> > > >> > manner > > >> > >> > > >> > > > inside mapblock() closure. Examples of that are > plenty > > >> > given > > >> > >> in > > >> > >> > > [2]. > > >> > >> > > >> > > > Essentially, in mapblock, you'd reform the row keys > to > > >> > >> reflect > > >> > >> > > >> cluster > > >> > >> > > >> > > > index in C. while going over A, you'd have a > "nearest > > >> > >> neighbor" > > >> > >> > > >> problem > > >> > >> > > >> > > to > > >> > >> > > >> > > > solve for the row of A and centroids C. This is the > > >> bulk of > > >> > >> > > >> computation > > >> > >> > > >> > > > really, and there are a few tricks there that can > > speed > > >> > this > > >> > >> > step > > >> > >> > > >> up in > > >> > >> > > >> > > > both exact and approximate manner, but you can start > > >> with a > > >> > >> > naive > > >> > >> > > >> > search. > > >> > >> > > >> > > > > > >> > >> > > >> > > > Centroid recomputation: > > >> > >> > > >> > > > once you assigned centroids to the keys of marix A, > > >> you'd > > >> > >> want > > >> > >> > to > > >> > >> > > >> do an > > >> > >> > > >> > > > aggregating transpose of A to compute essentially > > >> average > > >> > of > > >> > >> > row A > > >> > >> > > >> > > grouped > > >> > >> > > >> > > > by the centroid key. The trick is to do a > computation > > of > > >> > >> (1|A)' > > >> > >> > > >> which > > >> > >> > > >> > > will > > >> > >> > > >> > > > results in a matrix of the shape (Counts/sums of > > cluster > > >> > >> rows). > > >> > >> > > >> This is > > >> > >> > > >> > > the > > >> > >> > > >> > > > part i find difficult to explain without a latex > > >> graphics. > > >> > >> > > >> > > > > > >> > >> > > >> > > > In Samsara, construction of (1|A)' corresponds to > DRM > > >> > >> expression > > >> > >> > > >> > > > > > >> > >> > > >> > > > (1 cbind A).t (again, see [2]). > > >> > >> > > >> > > > > > >> > >> > > >> > > > So when you compute, say, > > >> > >> > > >> > > > > > >> > >> > > >> > > > B = (1 | A)', > > >> > >> > > >> > > > > > >> > >> > > >> > > > then B is (n+1) x k, so each column contains a > vector > > >> > >> > > corresponding > > >> > >> > > >> to > > >> > >> > > >> > a > > >> > >> > > >> > > > cluster 1..k. In such column, the first element > would > > >> be # > > >> > of > > >> > >> > > >> points in > > >> > >> > > >> > > the > > >> > >> > > >> > > > cluster, and the rest of it would correspond to sum > of > > >> all > > >> > >> > points. > > >> > >> > > >> So > > >> > >> > > >> > in > > >> > >> > > >> > > > order to arrive to an updated matrix C, we need to > > >> collect > > >> > B > > >> > >> > into > > >> > >> > > >> > memory, > > >> > >> > > >> > > > and slice out counters (first row) from the rest of > > it. > > >> > >> > > >> > > > > > >> > >> > > >> > > > So, to compute C: > > >> > >> > > >> > > > > > >> > >> > > >> > > > C <- B (2:,:) each row divided by B(1,:) > > >> > >> > > >> > > > > > >> > >> > > >> > > > (watch out for empty clusters with 0 elements, this > > will > > >> > >> cause > > >> > >> > > lack > > >> > >> > > >> of > > >> > >> > > >> > > > convergence and NaNs in the newly computed C). > > >> > >> > > >> > > > > > >> > >> > > >> > > > This operation obviously uses subblocking and > row-wise > > >> > >> iteration > > >> > >> > > >> over > > >> > >> > > >> > B, > > >> > >> > > >> > > > for which i am again making reference to [2]. > > >> > >> > > >> > > > > > >> > >> > > >> > > > > > >> > >> > > >> > > > [1] https://github.com/apache/ > > >> > mahout/blob/master/math-scala/ > > >> > >> > > >> > > > src/main/scala/org/apache/maho > > >> ut/math/drm/package.scala# > > >> > L149 > > >> > >> > > >> > > > > > >> > >> > > >> > > > [2], Sasmara manual, a bit dated but viable, > > >> > >> > http://apache.github > > >> > >> > > . > > >> > >> > > >> > > > io/mahout/doc/ScalaSparkBindings.html > > >> > >> > > >> > > > > > >> > >> > > >> > > > [3] scaladoc, again, dated but largely viable for > the > > >> > >> purpose of > > >> > >> > > >> this > > >> > >> > > >> > > > exercise: > > >> > >> > > >> > > > http://apache.github.io/mahout/0.10.1/docs/mahout- > > math- > > >> > >> > > >> scala/index.htm > > >> > >> > > >> > > > > > >> > >> > > >> > > > [4] mapblock etc. http://apache.github.io/mahout > > >> > >> > > >> /0.10.1/docs/mahout- > > >> > >> > > >> > > > math-scala/index.html#org.apache.mahout.math.drm. > > >> > RLikeDrmOps > > >> > >> > > >> > > > > > >> > >> > > >> > > > On Fri, Mar 31, 2017 at 9:54 AM, KHATWANI PARTH > > BHARAT < > > >> > >> > > >> > > > h2016...@pilani.bits-pilani.ac.in> wrote: > > >> > >> > > >> > > > > > >> > >> > > >> > > >> @Dmitriycan you please again tell me the approach > to > > >> move > > >> > >> > ahead. > > >> > >> > > >> > > >> > > >> > >> > > >> > > >> > > >> > >> > > >> > > >> Thanks > > >> > >> > > >> > > >> Parth Khatwani > > >> > >> > > >> > > >> > > >> > >> > > >> > > >> > > >> > >> > > >> > > >> On Fri, Mar 31, 2017 at 10:15 PM, KHATWANI PARTH > > >> BHARAT < > > >> > >> > > >> > > >> h2016...@pilani.bits-pilani.ac.in> wrote: > > >> > >> > > >> > > >> > > >> > >> > > >> > > >> > yes i am unable to figure out the way ahead. > > >> > >> > > >> > > >> > Like how to create the augmented matrix A := > (0|D) > > >> which > > >> > >> you > > >> > >> > > have > > >> > >> > > >> > > >> > mentioned. > > >> > >> > > >> > > >> > > > >> > >> > > >> > > >> > > > >> > >> > > >> > > >> > On Fri, Mar 31, 2017 at 10:10 PM, Dmitriy > Lyubimov > > < > > >> > >> > > >> > dlie...@gmail.com > > >> > >> > > >> > > > > > >> > >> > > >> > > >> > wrote: > > >> > >> > > >> > > >> > > > >> > >> > > >> > > >> >> was my reply for your post on @user has been a > bit > > >> > >> > confusing? > > >> > >> > > >> > > >> >> > > >> > >> > > >> > > >> >> On Fri, Mar 31, 2017 at 8:40 AM, KHATWANI PARTH > > >> BHARAT > > >> > < > > >> > >> > > >> > > >> >> h2016...@pilani.bits-pilani.ac.in> wrote: > > >> > >> > > >> > > >> >> > > >> > >> > > >> > > >> >> > Sir, > > >> > >> > > >> > > >> >> > I am trying to write the kmeans clustering > > >> algorithm > > >> > >> using > > >> > >> > > >> Mahout > > >> > >> > > >> > > >> >> Samsara > > >> > >> > > >> > > >> >> > but i am bit confused > > >> > >> > > >> > > >> >> > about how to leverage Distributed Row Matrix > for > > >> the > > >> > >> same. > > >> > >> > > Can > > >> > >> > > >> > > >> anybody > > >> > >> > > >> > > >> >> help > > >> > >> > > >> > > >> >> > me with same. > > >> > >> > > >> > > >> >> > > > >> > >> > > >> > > >> >> > > > >> > >> > > >> > > >> >> > > > >> > >> > > >> > > >> >> > > > >> > >> > > >> > > >> >> > > > >> > >> > > >> > > >> >> > Thanks > > >> > >> > > >> > > >> >> > Parth Khatwani > > >> > >> > > >> > > >> >> > > > >> > >> > > >> > > >> >> > > >> > >> > > >> > > >> > > > >> > >> > > >> > > >> > > > >> > >> > > >> > > >> > > >> > >> > > >> > > > > > >> > >> > > >> > > > > > >> > >> > > >> > > > > >> > >> > > >> > > > > >> > >> > > >> > > > >> > >> > > >> > > >> > >> > > > > > >> > >> > > > > > >> > >> > > > > >> > >> > > > >> > >> > > >> > > > > >> > > > > >> > > > >> > > > > > > > > >
