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Registered: ‎10-28-2013

NaN Error using arff.vector, canopy/kmeans and clusterdump

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/lib/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)
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[Masternode@Masterdatanode ~]$ mahout canopy -i /user/Masternode/seeds/seeds_data.arff.mvc -o /user/Masternode/seeds/output -dm org.apache.mahout.common.distance.EuclideanDistanceMeasure -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/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/lib/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.distance.EuclideanDistanceMeasure], --endPhase=[2147483647], --input=[/user/Masternode/seeds/seeds_data.arff.mvc], --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.EuclideanDistanceMeasure@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_1471946889250_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_1471946889250_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)
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[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.CosineDistanceMeasure
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/lib/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.distance.CosineDistanceMeasure], --endPhase=[2147483647], --input=[/user/Masternode/seeds/output/clusters-0-final], --output=[/tmp/seeds/canopy_dump3.txt], --outputFormat=[TEXT], --pointsDir=[/user/Masternode/seeds/output/clusteredPoints], --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/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/lib/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.distance.TanimotoDistanceMeasure], --endPhase=[2147483647], --input=[/user/Masternode/seeds/seeds_data.arff.mvc], --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/part-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/part-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_1471946889250_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_1471946889250_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)
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[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.TanimotoDistanceMeasure
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/lib/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.distance.TanimotoDistanceMeasure], --endPhase=[2147483647], --input=[/user/Masternode/seeds/kmeans-out/clusters-1-final], --output=[/tmp/seeds/canopy_dump4.txt], --outputFormat=[TEXT], --pointsDir=[/user/Masternode/seeds/kmeans-out/clusteredPoints], --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
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Posts: 13
Registered: ‎10-28-2013

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

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)

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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/lib/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

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[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.arff.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)

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxMAHOUT_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/lib/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

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[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)

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
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/lib/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/clusteredPoints -dm org.apache.mahout.common.distance.TanimotoDistanceMeasure
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/lib/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.distance.TanimotoDistanceMeasure], --endPhase=[2147483647], --input=[/user/Masternode/balance/kmeans-out/clusters-1-final], --output=[/tmp/balance/balance_clusters.txt], --outputFormat=[TEXT], --pointsDir=[/user/Masternode/balance/kmeans-out/clusteredPoints], --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)

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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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