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08-23-2016 08:00 PM

Hello Cloudera

I have been using the CLI arff.vector commands for creating vectors for kmeans and canopy clustering.

This works on occassion (so far 1 out of 3 times) But often I get all my clusterdump output as NaN.

All my mahout commands are successful and no errors are reported

Here are the mahout arff.vector, canopy and clusterdump CLI commands

[Masternode@Masterdatanode ~]$ mahout arff.vector -d /tmp/seeds/seeds_data.arff -o /user/Masternode/seeds/ -t /tmp/seeds/dict.txt MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath. Running on hadoop, using /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/lib/hadoop/bin/hadoop and HADOOP_CONF_DIR=/etc/hadoop/conf MAHOUT-JOB: /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/mahout/mahout-examples-0.9-cdh5.6.0-job.jar 16/08/23 12:35:24 WARN driver.MahoutDriver: No arff.vector.props found on classpath, will use command-line arguments only 16/08/23 12:35:24 INFO arff.Driver: Output Dir: /user/Masternode/seeds/ 16/08/23 12:35:24 INFO arff.Driver: Converting File: /tmp/seeds/seeds_data.arff 16/08/23 12:35:27 INFO zlib.ZlibFactory: Successfully loaded & initialized native-zlib library 16/08/23 12:35:27 INFO compress.CodecPool: Got brand-new compressor [.deflate] 16/08/23 12:35:28 INFO arff.Driver: Wrote: 210 vectors 16/08/23 12:35:28 INFO driver.MahoutDriver: Program took 4063 ms (Minutes: 0.06771666666666666) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx [Masternode@Masterdatanode ~]$ mahout canopy -i /user/Masternode/seeds/seeds_data.arff.mvc -o /user/Masternode/seeds/output -dm org.apache.mahout.common.distance.EuclideanDistanc eMeasure -t1 1 -t2 2 -xm mapreduce -cl -ow MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath. Running on hadoop, using /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/hadoop/bin/hadoop and HADOOP_CONF_DIR=/etc/hadoop/conf MAHOUT-JOB: /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/mahout/mahout-examples-0.9-cdh5.6.0-job.jar 16/08/23 12:45:16 WARN driver.MahoutDriver: No canopy.props found on classpath, will use command-line arguments only 16/08/23 12:45:16 INFO common.AbstractJob: Command line arguments: {--clustering=null, --distanceMeasure=[org.apache.mahout.common.distan ce.EuclideanDistanceMeasure], --endPhase=[2147483647], --input=[/user/Masternode/seeds/seeds_data.arff.mv c], --method=[mapreduce], --output=[/user/Masternode/seeds/output], --overwrite=null, --startPhase=[0], --t1=[1], --t2=[2], --tempDir=[temp]} 16/08/23 12:45:19 INFO canopy.CanopyDriver: Build Clusters Input: /user/Masternode/seeds/seeds_data.arff.mvc Out: /user/Masternode/seeds/output Measure: org.apache.mahout.common.distance.EuclideanDistanc eMeasure@7e40f6d2 t1: 1.0 t2: 2.0 16/08/23 12:45:20 INFO client.RMProxy: Connecting to ResourceManager at childnode2/192.168.1.10:8032 16/08/23 12:45:23 INFO input.FileInputFormat: Total input paths to process : 1 16/08/23 12:45:24 INFO mapreduce.JobSubmitter: number of splits:1 16/08/23 12:45:24 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1471946889250_0001 16/08/23 12:45:25 INFO impl.YarnClientImpl: Submitted application application_1471946889250_0001 16/08/23 12:45:25 INFO mapreduce.Job: The url to track the job: http://CHILDNODE2:8088/proxy/application_147194688 9250_0001/ 16/08/23 12:45:25 INFO mapreduce.Job: Running job: job_1471946889250_0001 16/08/23 12:45:37 INFO mapreduce.Job: Job job_1471946889250_0001 running in uber mode : false 16/08/23 12:45:37 INFO mapreduce.Job: map 0% reduce 0% 16/08/23 12:45:55 INFO mapreduce.Job: map 100% reduce 0% 16/08/23 12:46:07 INFO mapreduce.Job: map 100% reduce 100% 16/08/23 12:46:08 INFO mapreduce.Job: Job job_1471946889250_0001 completed successfully 16/08/23 12:46:08 INFO mapreduce.Job: Counters: 49 File System Counters FILE: Number of bytes read=864 FILE: Number of bytes written=235727 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=7915 HDFS: Number of bytes written=86916 HDFS: Number of read operations=7 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=1 Launched reduce tasks=1 Data-local map tasks=1 Total time spent by all maps in occupied slots (ms)=14544 Total time spent by all reduces in occupied slots (ms)=9207 Total time spent by all map tasks (ms)=14544 Total time spent by all reduce tasks (ms)=9207 Total vcore-seconds taken by all map tasks=14544 Total vcore-seconds taken by all reduce tasks=9207 Total megabyte-seconds taken by all map tasks=14893056 Total megabyte-seconds taken by all reduce tasks=9427968 Map-Reduce Framework Map input records=210 Map output records=210 Map output bytes=15750 Map output materialized bytes=860 Input split bytes=129 Combine input records=0 Combine output records=0 Reduce input groups=1 Reduce shuffle bytes=860 Reduce input records=210 Reduce output records=210 Spilled Records=420 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=194 CPU time spent (ms)=4210 Physical memory (bytes) snapshot=651390976 Virtual memory (bytes) snapshot=3144257536 Total committed heap usage (bytes)=618659840 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 File Input Format Counters Bytes Read=7786 File Output Format Counters Bytes Written=86916 16/08/23 12:46:08 INFO client.RMProxy: Connecting to ResourceManager at childnode2/192.168.1.10:8032 16/08/23 12:46:11 INFO input.FileInputFormat: Total input paths to process : 1 16/08/23 12:46:11 INFO mapreduce.JobSubmitter: number of splits:1 16/08/23 12:46:11 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1471946889250_0002 16/08/23 12:46:11 INFO impl.YarnClientImpl: Submitted application application_1471946889250_0002 16/08/23 12:46:11 INFO mapreduce.Job: The url to track the job: http://CHILDNODE2:8088/proxy/application_147194688 9250_0002/ 16/08/23 12:46:11 INFO mapreduce.Job: Running job: job_1471946889250_0002 16/08/23 12:46:24 INFO mapreduce.Job: Job job_1471946889250_0002 running in uber mode : false 16/08/23 12:46:24 INFO mapreduce.Job: map 0% reduce 0% 16/08/23 12:46:37 INFO mapreduce.Job: map 100% reduce 0% 16/08/23 12:46:37 INFO mapreduce.Job: Job job_1471946889250_0002 completed successfully 16/08/23 12:46:37 INFO mapreduce.Job: Counters: 30 File System Counters FILE: Number of bytes read=0 FILE: Number of bytes written=116782 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=95033 HDFS: Number of bytes written=128 HDFS: Number of read operations=13 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=1 Data-local map tasks=1 Total time spent by all maps in occupied slots (ms)=11335 Total time spent by all reduces in occupied slots (ms)=0 Total time spent by all map tasks (ms)=11335 Total vcore-seconds taken by all map tasks=11335 Total megabyte-seconds taken by all map tasks=11607040 Map-Reduce Framework Map input records=210 Map output records=0 Input split bytes=129 Spilled Records=0 Failed Shuffles=0 Merged Map outputs=0 GC time elapsed (ms)=86 CPU time spent (ms)=2060 Physical memory (bytes) snapshot=190013440 Virtual memory (bytes) snapshot=1557372928 Total committed heap usage (bytes)=175112192 File Input Format Counters Bytes Read=7786 File Output Format Counters Bytes Written=128 16/08/23 12:46:37 INFO driver.MahoutDriver: Program took 81076 ms (Minutes: 1.3512666666666666) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx [Masternode@Masterdatanode ~]$ mahout clusterdump -i /user/Masternode/seeds/output/clusters-0-final -o /tmp/seeds/canopy_dump3.txt -p /user/Masternode/seeds/output/clusteredPoints -dm org.apache.mahout.common.distance.CosineDistanceMe asure MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath. Running on hadoop, using /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/hadoop/bin/hadoop and HADOOP_CONF_DIR=/etc/hadoop/conf MAHOUT-JOB: /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/mahout/mahout-examples-0.9-cdh5.6.0-job.jar 16/08/23 13:22:57 WARN driver.MahoutDriver: No clusterdump.props found on classpath, will use command-line arguments only 16/08/23 13:22:57 INFO common.AbstractJob: Command line arguments: {--dictionaryType=[text], --distanceMeasure=[org.apache.mahout.common.distan ce.CosineDistanceMeasure], --endPhase=[2147483647], --input=[/user/Masternode/seeds/output/clusters-0- final], --output=[/tmp/seeds/canopy_dump3.txt], --outputFormat=[TEXT], --pointsDir=[/user/Masternode/seeds/output/cluster edPoints], --startPhase=[0], --tempDir=[temp]} 16/08/23 13:23:00 INFO clustering.ClusterDumper: Wrote 210 clusters 16/08/23 13:23:00 INFO driver.MahoutDriver: Program took 3653 ms

The kmeans and clusterdump commands are also successful using the same arff.mvc input file

[Masternode@Masterdatanode ~]$ mahout kmeans -i /user/Masternode/seeds/seeds_data.arff.mvc -c /user/Masternode/seeds/output/clusters-0-final -o /user/Masternode/seeds/kmeans-out -x 20 -k 3 -dm org.apache.mahout.common.distance.TanimotoDistanceMeasure -xm mapreduce -ow -cl MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath. Running on hadoop, using /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/hadoop/bin/hadoop and HADOOP_CONF_DIR=/etc/hadoop/conf MAHOUT-JOB: /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/mahout/mahout-examples-0.9-cdh5.6.0-job.jar 16/08/23 13:36:39 WARN driver.MahoutDriver: No kmeans.props found on classpath, will use command-line arguments only 16/08/23 13:36:39 INFO common.AbstractJob: Command line arguments: {--clustering=null, --clusters=[/user/Masternode/seeds/output/clusters -0-final], --convergenceDelta=[0.5], --distanceMeasure=[org.apache.mahout.common.distan ce.TanimotoDistanceMeasure], --endPhase=[2147483647], --input=[/user/Masternode/seeds/seeds_data.arff.mv c], --maxIter=[20], --method=[mapreduce], --numClusters=[3], --output=[/user/Masternode/seeds/kmeans-out], --overwrite=null, --startPhase=[0], --tempDir=[temp]} 16/08/23 13:36:42 INFO common.HadoopUtil: Deleting /user/Masternode/seeds/output/clusters-0-final 16/08/23 13:36:42 INFO zlib.ZlibFactory: Successfully loaded & initialized native-zlib library 16/08/23 13:36:42 INFO compress.CodecPool: Got brand-new compressor [.deflate] 16/08/23 13:36:42 INFO compress.CodecPool: Got brand-new decompressor [.deflate] 16/08/23 13:36:42 INFO kmeans.RandomSeedGenerator: Wrote 3 Klusters to /user/Masternode/seeds/output/clusters-0-final/par t-randomSeed 16/08/23 13:36:43 INFO kmeans.KMeansDriver: Input: /user/Masternode/seeds/seeds_data.arff.mvc Clusters In: /user/Masternode/seeds/output/clusters-0-final/par t-randomSeed Out: /user/Masternode/seeds/kmeans-out 16/08/23 13:36:43 INFO kmeans.KMeansDriver: convergence: 0.5 max Iterations: 20 16/08/23 13:36:43 INFO client.RMProxy: Connecting to ResourceManager at childnode2/192.168.1.10:8032 16/08/23 13:36:46 INFO input.FileInputFormat: Total input paths to process : 1 16/08/23 13:36:46 INFO mapreduce.JobSubmitter: number of splits:1 16/08/23 13:36:47 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1471946889250_0013 16/08/23 13:36:47 INFO impl.YarnClientImpl: Submitted application application_1471946889250_0013 16/08/23 13:36:47 INFO mapreduce.Job: The url to track the job: http://childnode2:8088/proxy/application_147194688 9250_0013/ 16/08/23 13:36:47 INFO mapreduce.Job: Running job: job_1471946889250_0013 16/08/23 13:36:57 INFO mapreduce.Job: Job job_1471946889250_0013 running in uber mode : false 16/08/23 13:36:57 INFO mapreduce.Job: map 0% reduce 0% 16/08/23 13:37:11 INFO mapreduce.Job: map 100% reduce 0% 16/08/23 13:37:24 INFO mapreduce.Job: map 100% reduce 100% 16/08/23 13:37:25 INFO mapreduce.Job: Job job_1471946889250_0013 completed successfully 16/08/23 13:37:26 INFO mapreduce.Job: Counters: 49 File System Counters FILE: Number of bytes read=248 FILE: Number of bytes written=233027 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=11435 HDFS: Number of bytes written=1340 HDFS: Number of read operations=25 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=1 Launched reduce tasks=1 Data-local map tasks=1 Total time spent by all maps in occupied slots (ms)=12511 Total time spent by all reduces in occupied slots (ms)=10953 Total time spent by all map tasks (ms)=12511 Total time spent by all reduce tasks (ms)=10953 Total vcore-seconds taken by all map tasks=12511 Total vcore-seconds taken by all reduce tasks=10953 Total megabyte-seconds taken by all map tasks=12811264 Total megabyte-seconds taken by all reduce tasks=11215872 Map-Reduce Framework Map input records=210 Map output records=3 Map output bytes=1203 Map output materialized bytes=244 Input split bytes=129 Combine input records=0 Combine output records=0 Reduce input groups=3 Reduce shuffle bytes=244 Reduce input records=3 Reduce output records=3 Spilled Records=6 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=184 CPU time spent (ms)=4390 Physical memory (bytes) snapshot=659808256 Virtual memory (bytes) snapshot=3141287936 Total committed heap usage (bytes)=618135552 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 File Input Format Counters Bytes Read=7786 File Output Format Counters Bytes Written=1340 16/08/23 13:37:26 INFO kmeans.KMeansDriver: Clustering data 16/08/23 13:37:26 INFO kmeans.KMeansDriver: Running Clustering 16/08/23 13:37:26 INFO kmeans.KMeansDriver: Input: /user/Masternode/seeds/seeds_data.arff.mvc Clusters In: /user/Masternode/seeds/kmeans-out Out: /user/Masternode/seeds/kmeans-out 16/08/23 13:37:26 INFO client.RMProxy: Connecting to ResourceManager at childnode2/192.168.1.10:8032 16/08/23 13:37:29 INFO input.FileInputFormat: Total input paths to process : 1 16/08/23 13:37:29 INFO mapreduce.JobSubmitter: number of splits:1 16/08/23 13:37:29 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1471946889250_0014 16/08/23 13:37:29 INFO impl.YarnClientImpl: Submitted application application_1471946889250_0014 16/08/23 13:37:29 INFO mapreduce.Job: The url to track the job: http://childnode2:8088/proxy/application_147194688 9250_0014/ 16/08/23 13:37:29 INFO mapreduce.Job: Running job: job_1471946889250_0014 16/08/23 13:37:39 INFO mapreduce.Job: Job job_1471946889250_0014 running in uber mode : false 16/08/23 13:37:39 INFO mapreduce.Job: map 0% reduce 0% 16/08/23 13:37:52 INFO mapreduce.Job: map 100% reduce 0% 16/08/23 13:37:53 INFO mapreduce.Job: Job job_1471946889250_0014 completed successfully 16/08/23 13:37:53 INFO mapreduce.Job: Counters: 30 File System Counters FILE: Number of bytes read=0 FILE: Number of bytes written=116021 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=9449 HDFS: Number of bytes written=22378 HDFS: Number of read operations=13 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Launched map tasks=1 Data-local map tasks=1 Total time spent by all maps in occupied slots (ms)=10747 Total time spent by all reduces in occupied slots (ms)=0 Total time spent by all map tasks (ms)=10747 Total vcore-seconds taken by all map tasks=10747 Total megabyte-seconds taken by all map tasks=11004928 Map-Reduce Framework Map input records=210 Map output records=210 Input split bytes=129 Spilled Records=0 Failed Shuffles=0 Merged Map outputs=0 GC time elapsed (ms)=86 CPU time spent (ms)=1460 Physical memory (bytes) snapshot=185024512 Virtual memory (bytes) snapshot=1555386368 Total committed heap usage (bytes)=175112192 File Input Format Counters Bytes Read=7786 File Output Format Counters Bytes Written=22378 16/08/23 13:37:53 INFO driver.MahoutDriver: Program took 74114 ms (Minutes: 1.2352333333333334) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx [Masternode@Masterdatanode ~]$ mahout clusterdump -i /user/Masternode/seeds/kmeans-out/clusters-1-final -o /tmp/seeds/canopy_dump4.txt -p /user/Masternode/seeds/kmeans-out/clusteredPoints -dm org.apache.mahout.common.distance.TanimotoDistance Measure MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath. Running on hadoop, using /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/hadoop/bin/hadoop and HADOOP_CONF_DIR=/etc/hadoop/conf MAHOUT-JOB: /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/mahout/mahout-examples-0.9-cdh5.6.0-job.jar 16/08/23 13:41:23 WARN driver.MahoutDriver: No clusterdump.props found on classpath, will use command-line arguments only 16/08/23 13:41:23 INFO common.AbstractJob: Command line arguments: {--dictionaryType=[text], --distanceMeasure=[org.apache.mahout.common.distan ce.TanimotoDistanceMeasure], --endPhase=[2147483647], --input=[/user/Masternode/seeds/kmeans-out/cluster s-1-final], --output=[/tmp/seeds/canopy_dump4.txt], --outputFormat=[TEXT], --pointsDir=[/user/Masternode/seeds/kmeans-out/clu steredPoints], --startPhase=[0], --tempDir=[temp]} 16/08/23 13:41:27 INFO clustering.ClusterDumper: Wrote 3 clusters 16/08/23 13:41:27 INFO driver.MahoutDriver: Program took 3894 ms (Minutes: 0.0649)

However when I inspect the clusterdump output for the canopy centroids I find all NaN values

C-0{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-1{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-2{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-3{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-4{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-5{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-6{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, 5.000, 3.000] r=[]} C-7{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-8{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-9{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-10{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-11{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-12{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-13{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-14{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-15{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-16{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-17{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-18{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-19{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-20{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-21{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-22{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-23{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-24{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-25{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-26{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-27{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-28{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-29{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-30{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-31{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-32{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-33{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-34{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-35{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-36{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-37{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, 5.000, 3.000] r=[]} C-38{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-39{n=1 c=[NaN, 13.000, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-40{n=1 c=[NaN, 13.000, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-41{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-42{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-43{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-44{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-45{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-46{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-47{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-48{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-49{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-50{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-51{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-52{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-53{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-54{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-55{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-56{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-57{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-58{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-59{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-60{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-61{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-62{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-63{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-64{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-65{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-66{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-67{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-68{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-69{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] r=[]} C-70{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-71{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-72{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-73{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-74{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-75{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-76{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-77{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-78{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-79{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-80{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-81{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-82{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-83{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-84{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-85{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-86{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-87{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-88{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-89{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-90{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-91{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-92{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-93{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-94{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-95{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-96{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-97{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-98{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-99{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-100{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-101{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-102{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-103{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-104{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-105{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-106{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-107{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-108{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-109{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-110{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-111{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-112{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-113{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-114{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-115{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-116{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-117{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-118{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-119{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-120{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-121{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-122{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-123{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-124{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-125{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-126{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-127{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-128{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-129{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-130{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-131{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-132{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-133{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-134{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-135{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-136{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-137{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-138{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-139{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} C-140{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-141{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-142{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-143{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-144{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-145{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-146{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-147{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-148{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-149{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-150{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-151{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-152{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-153{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-154{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-155{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-156{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-157{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-158{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-159{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-160{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-161{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-162{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-163{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-164{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-165{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-166{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-167{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-168{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-169{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-170{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-171{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-172{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-173{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-174{n=1 c=[NaN, 15.000, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-175{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-176{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-177{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-178{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-179{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-180{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-181{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-182{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-183{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-184{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-185{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-186{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-187{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-188{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-189{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-190{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-191{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-192{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-193{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-194{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-195{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-196{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-197{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-198{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-199{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-200{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-201{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-202{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, 5.000, 1.000] r=[]} C-203{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-204{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-205{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-206{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-207{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-208{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]} C-209{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] r=[]}

And for the kmeans clusters again I find all NaN values

VL-74{n=211 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 0.815]} Weight : [props - optional]: Point: 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, 5.000, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, 15.000, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 1.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, 13.000, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, 13.000, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, 5.000, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, 5.000, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] 1.0 : [distance=0.0]: [NaN, NaN, NaN, NaN, NaN, NaN, NaN, 3.000] VL-70{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]} VL-97{n=1 c=[NaN, NaN, NaN, NaN, NaN, NaN, NaN, 2.000] r=[]}

The dataset here is the seeds dataset, in arff form, available from the UCI Dataset Repository at https://archive.ics.uci.edu/ml/datasets/seeds It has 8 attributes including the target variable.

This also happened with my iris.arff dataset of similar size. Only my balance.arff (UCI Balance scale) dataset, another small dataset, obtained good cluster and centroid values. This error also happened with a larger dataset of 5000 vectors.

Where is the error ? In the arff.vector or canopy/kmeans cluster or clusterdump commands ?

All these commands seem to process okay !!!

Highlighted
## Re: NaN Error using arff.vector, canopy/kmeans and clusterdump

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08-25-2016 08:09 PM

Hello Cloudera

I have an update on my NAN problem

I have discovered I can use mahout seqdumper to view the vectors written by the mahout arff.vector command to see whether or not it is actualy writing the vectors properly.

I checked all three files: iris.arff.mvc, seeds.arff.mvc and balance.arff.mvc using mahout seqdumper.

It turns out that in fact it was the mahout.arff.vector creating the NaN output error which was transferred to my kmeans/canopy and clusterdump output.

Here we can see my seqdumper output for my seeds and iris dataset and my balance scale dataset (which works okay)

[Masternode@Masterdatanode ~]$ mahout seqdumper -i /user/Masternode/seeds/seeds_data.arff.mvc > /tmp/seeds/dump.txt 16/08/26 01:52:49 WARN driver.MahoutDriver: No seqdumper.props found on classpath, will use command-line arguments only 16/08/26 01:52:50 INFO common.AbstractJob: Command line arguments: {--endPhase=[2147483647], --input=[/user/Masternode/seeds/seeds_data.arff.mvc], --startPhase=[0], --tempDir=[temp]} 16/08/26 01:52:53 INFO zlib.ZlibFactory: Successfully loaded & initialized native-zlib library 16/08/26 01:52:53 INFO compress.CodecPool: Got brand-new decompressor [.deflate] 16/08/26 01:52:53 INFO driver.MahoutDriver: Program took 3827 ms (Minutes: 0.06378333333333333) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxx MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath. Running on hadoop, using /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/hadoop/bin/hadoop and HADOOP_CONF_DIR=/etc/hadoop/conf MAHOUT-JOB: /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/mahout/mahout-examples-0.9-cdh5.6.0-job.jar Input Path: /user/Masternode/seeds/seeds_data.arff.mvc Key class: class org.apache.hadoop.io.LongWritable Value Class: class org.apache.mahout.math.VectorWritable Key: 0: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:NaN,5:NaN,6:NaN,7:1.0} Key: 1: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:NaN,5:NaN,6:NaN,7:1.0} Key: 2: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:NaN,5:NaN,6:NaN,7:1.0} Key: 3: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:NaN,5:NaN,6:NaN,7:1.0} Key: 4: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:NaN,5:NaN,6:NaN,7:1.0} : : : : Key: 205: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:NaN,5:NaN,6:NaN,7:3.0} Key: 206: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:NaN,5:NaN,6:NaN,7:3.0} Key: 207: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:NaN,5:NaN,6:NaN,7:3.0} Key: 208: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:NaN,5:NaN,6:NaN,7:3.0} Key: 209: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:NaN,5:NaN,6:NaN,7:3.0} Count: 210 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxx [Masternode@Masterdatanode ~]$ mahout seqdumper -i /user/Masternode/iris_data/kmeans3/iris.arff.mvc > /tmp/iris_data/dump.txt 16/08/26 03:52:28 WARN driver.MahoutDriver: No seqdumper.props found on classpath, will use command-line arguments only 16/08/26 03:52:29 INFO common.AbstractJob: Command line arguments: {--endPhase=[2147483647], --input=[/user/Masternode/iris_data/kmeans3/iris.a rff.mvc], --startPhase=[0], --tempDir=[temp]} 16/08/26 03:52:32 INFO zlib.ZlibFactory: Successfully loaded & initialized native-zlib library 16/08/26 03:52:32 INFO compress.CodecPool: Got brand-new decompressor [.deflate] 16/08/26 03:52:32 INFO driver.MahoutDriver: Program took 3746 ms (Minutes: 0.062433333333333334) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxxMAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath. Running on hadoop, using /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/hadoop/bin/hadoop and HADOOP_CONF_DIR=/etc/hadoop/conf MAHOUT-JOB: /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/mahout/mahout-examples-0.9-cdh5.6.0-job.jar Input Path: /user/Masternode/iris_data/kmeans3/iris.arff.mvc Key class: class org.apache.hadoop.io.LongWritable Value Class: class org.apache.mahout.math.VectorWritable Key: 0: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:1.0} Key: 1: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:1.0} Key: 2: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:1.0} Key: 3: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:1.0} Key: 4: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:1.0} : : : : Key: 145: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:3.0} Key: 146: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:3.0} Key: 147: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:3.0} Key: 148: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:3.0} Key: 149: Value: {0:NaN,1:NaN,2:NaN,3:NaN,4:3.0} Count: 150 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxx [Masternode@Masterdatanode ~]$ mahout seqdumper -i /user/Masternode/balance/balance.arff.mvc > /tmp/balance/dump.txt 16/08/26 01:58:33 WARN driver.MahoutDriver: No seqdumper.props found on classpath, will use command-line arguments only 16/08/26 01:58:34 INFO common.AbstractJob: Command line arguments: {--endPhase=[2147483647], --input=[/user/Masternode/balance/balance.arff.mvc ], --startPhase=[0], --tempDir=[temp]} 16/08/26 01:58:37 INFO zlib.ZlibFactory: Successfully loaded & initialized native-zlib library 16/08/26 01:58:37 INFO compress.CodecPool: Got brand-new decompressor [.deflate] 16/08/26 01:58:37 INFO driver.MahoutDriver: Program took 3889 ms (Minutes: 0.06481666666666666) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxx MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath. Running on hadoop, using /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/hadoop/bin/hadoop and HADOOP_CONF_DIR=/etc/hadoop/conf MAHOUT-JOB: /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/mahout/mahout-examples-0.9-cdh5.6.0-job.jar Input Path: /user/Masternode/balance/balance.arff.mvc Key class: class org.apache.hadoop.io.LongWritable Value Class: class org.apache.mahout.math.VectorWritable Key: 0: Value: {0:1.0,1:1.0,2:1.0,3:1.0,4:2.0} Key: 1: Value: {0:1.0,1:1.0,2:1.0,3:2.0,4:3.0} Key: 2: Value: {0:1.0,1:1.0,2:1.0,3:3.0,4:3.0} Key: 3: Value: {0:1.0,1:1.0,2:1.0,3:4.0,4:3.0} Key: 4: Value: {0:1.0,1:1.0,2:1.0,3:5.0,4:3.0} : : : : Key: 620: Value: {0:5.0,1:5.0,2:5.0,3:1.0,4:1.0} Key: 621: Value: {0:5.0,1:5.0,2:5.0,3:2.0,4:1.0} Key: 622: Value: {0:5.0,1:5.0,2:5.0,3:3.0,4:1.0} Key: 623: Value: {0:5.0,1:5.0,2:5.0,3:4.0,4:1.0} Key: 624: Value: {0:5.0,1:5.0,2:5.0,3:5.0,4:2.0} Count: 625

Here are the clusters for the balance scale dataset

Masternode@Masterdatanode ~]$ mahout clusterdump -i /user/Masternode/balance/kmeans-out/clusters-1-final -o /tmp/balance/balance_clusters.txt -p /user/Masternode/balance/kmeans-out/clusteredPoint s -dm org.apache.mahout.common.distance.TanimotoDistance Measure MAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath. Running on hadoop, using /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/hadoop/bin/hadoop and HADOOP_CONF_DIR=/etc/hadoop/conf MAHOUT-JOB: /opt/cloudera/parcels/CDH-5.6.0-1.cdh5.6.0.p0.45/l ib/mahout/mahout-examples-0.9-cdh5.6.0-job.jar 16/08/22 23:05:40 WARN driver.MahoutDriver: No clusterdump.props found on classpath, will use command-line arguments only 16/08/22 23:05:40 INFO common.AbstractJob: Command line arguments: {--dictionaryType=[text], --distanceMeasure=[org.apache.mahout.common.distan ce.TanimotoDistanceMeasure], --endPhase=[2147483647], --input=[/user/Masternode/balance/kmeans-out/clust ers-1-final], --output=[/tmp/balance/balance_clusters.txt], --outputFormat=[TEXT], --pointsDir=[/user/Masternode/balance/kmeans-out/c lusteredPoints], --startPhase=[0], --tempDir=[temp]} 16/08/22 23:05:44 INFO clustering.ClusterDumper: Wrote 3 clusters 16/08/22 23:05:44 INFO driver.MahoutDriver: Program took 4136 ms (Minutes: 0.06893333333333333) xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx xxxxxxxxxxxxxxxxxx VL-410{n=213 c=[4.038, 2.131, 2.746, 2.446, 1.737] r=[0.968, 1.058, 1.361, 1.287, 0.917]} Weight : [props - optional]: Point: 1.0 : [distance=0.39346254907223044]: [2.000, 1.000, 1.000, 1.000, 1.000] 1.0 : [distance=0.29099665375325723]: [2.000, 1.000, 1.000, 2.000, 2.000] 1.0 : [distance=0.2737469670505256]: [2.000, 1.000, 2.000, 1.000, 2.000] 1.0 : [distance=0.2602217061100983]: [2.000, 1.000, 3.000, 1.000, 3.000] 1.0 : [distance=0.2703429967956935]: [2.000, 1.000, 4.000, 1.000, 3.000] : : : : VL-82{n=275 c=[1.975, 3.033, 2.669, 3.676, 2.415] r=[1.011, 1.379, 1.344, 1.242, 0.875]} Weight : [props - optional]: Point: 1.0 : [distance=0.4843955662442676]: [1.000, 1.000, 1.000, 1.000, 2.000] 1.0 : [distance=0.3310139832226746]: [1.000, 1.000, 1.000, 2.000, 3.000] 1.0 : [distance=0.25039239421781456]: [1.000, 1.000, 1.000, 3.000, 3.000] 1.0 : [distance=0.21916087567042697]: [1.000, 1.000, 1.000, 4.000, 3.000] 1.0 : [distance=0.23026798575608354]: [1.000, 1.000, 1.000, 5.000, 3.000] : : : : VL-370{n=140 c=[3.429, 4.271, 4.043, 2.486, 1.579] r=[1.283, 0.877, 1.095, 1.344, 0.854]} Weight : [props - optional]: Point: 1.0 : [distance=0.291345734798266]: [1.000, 2.000, 5.000, 1.000, 3.000] 1.0 : [distance=0.22857469129979302]: [1.000, 3.000, 4.000, 1.000, 3.000] 1.0 : [distance=0.22469106732898325]: [1.000, 3.000, 5.000, 1.000, 3.000] 1.0 : [distance=0.18798153075739144]: [1.000, 3.000, 5.000, 2.000, 3.000] 1.0 : [distance=0.27974934890340164]: [1.000, 4.000, 2.000, 1.000, 1.000] : : : :

Further on inspecting my balance.arff dataset.

I noticed that the file data were only integers seperated by commas ie

@relation balance-scale @attribute left-weight numeric @attribute left-distance numeric @attribute right-weight numeric @attribute right-distance numeric @attribute class { L, B, R} @data 1,1,1,1,B 1,1,1,2,R 1,1,1,3,R 1,1,1,4,R 1,1,1,5,R 1,1,2,1,R 1,1,2,2,R 1,1,2,3,R 1,1,2,4,R 1,1,2,5,R : :

Whereas my other datasets had doubles and float values as the data

ie for seeds.arff dataset and iris.arff dataset

@relation seeds @attribute area numeric @attribute perimeter numeric @attribute compactness numeric @attribute kernel-length numeric @attribute kernel-width numeric @attribute asymmetry numeric @attribute kernel-groove numeric @attribute class { 1, 2, 3} @data 15.26,14.84,0.871,5.763,3.312,2.221,5.22,1 14.88,14.57,0.8811,5.554,3.333,1.018,4.956,1 14.29,14.09,0.905,5.291,3.337,2.699,4.825,1 13.84,13.94,0.8955,5.324,3.379,2.259,4.805,1 16.14,14.99,0.9034,5.658,3.562,1.355,5.175,1 14.38,14.21,0.8951,5.386,3.312,2.462,4.956,1 14.69,14.49,0.8799,5.563,3.259,3.586,5.219,1 14.11,14.1,0.8911,5.42,3.302,2.7,5,1 16.63,15.46,0.8747,6.053,3.465,2.04,5.877,1 16.44,15.25,0.888,5.884,3.505,1.969,5.533,1 : : xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx @RELATION iris @ATTRIBUTE sepallength numeric @ATTRIBUTE sepalwidth numeric @ATTRIBUTE petallength numeric @ATTRIBUTE petalwidth numeric @ATTRIBUTE class {Iris-setosa,Iris-versicolor,Iris-virginica} @DATA 5.1,3.5,1.4,0.2,Iris-setosa 4.9,3.0,1.4,0.2,Iris-setosa 4.7,3.2,1.3,0.2,Iris-setosa 4.6,3.1,1.5,0.2,Iris-setosa 5.0,3.6,1.4,0.2,Iris-setosa 5.4,3.9,1.7,0.4,Iris-setosa 4.6,3.4,1.4,0.3,Iris-setosa 5.0,3.4,1.5,0.2,Iris-setosa 4.4,2.9,1.4,0.2,Iris-setosa 4.9,3.1,1.5,0.1,Iris-setosa : :

So THIS is what is causing the problem for the mahout arff.vector command.

It does not seem to like these double and float input data.

Is there any solution to this ???????????????????

I am using Cloudera CDH5 Version 5.6.0-1.cdh5.6.0.p0.45

and Mahout Version 0.9+cdh5.6.0+26

ANY HELP MOST WELCOLME !!!!!!!!!!!!!!!!!!!!!!!!!!

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