Hi ,
  The way you have installed mahout may be wrong .. can I know whether out
of box in mahout is working fine .
Syed Abdul kather
send from Samsung S3
On Jul 28, 2012 5:35 AM, "k6.amruta [via Lucene]" <
[email protected]> wrote:

> I am trying to run Wikipedia Bayes Example from
> https://cwiki.apache.org/confluence/...+Bayes+Example
>
> When I ran the following command : $MAHOUT_HOME/bin/mahout
> wikipediaXMLSplitter -d
> $MAHOUT_HOME/examples/temp/enwiki-latest-pages-articles10.xml -o
> wikipedia/chunks -c 64
>
> I am getting this error:
>
> Exception in thread "main" java.lang.NoClassDefFoundError: classpath
> Caused by: java.lang.ClassNotFoundException: classpath
>         at java.net.URLClassLoader$1.run(URLClassLoader.java:217)
>         at java.security.AccessController.doPrivileged(Native Method)
>         at java.net.URLClassLoader.findClass(URLClassLoader.java:205)
>         at java.lang.ClassLoader.loadClass(ClassLoader.java:323)
>         at sun.misc.Launcher$AppClassLoader.loadClass(Launcher.java:294)
>         at java.lang.ClassLoader.loadClass(ClassLoader.java:268)
>         at java.lang.ClassLoader.loadClassInternal(ClassLoader.java:336)
> Running on hadoop, using /x/home/hadoop_adm/opt/hadoop/bin/hadoop and
> HADOOP_CONF_DIR=
> MAHOUT-JOB:
> /x/home/user/mahout-distribution-0.7/mahout-examples-0.7-job.jar
> 12/07/27 16:28:02 WARN driver.MahoutDriver: Unable to add class:
> wikipediaXMLSplitter
> 12/07/27 16:28:02 WARN driver.MahoutDriver: No wikipediaXMLSplitter.props
> found on classpath, will use command-line arguments only
> Unknown program 'wikipediaXMLSplitter' chosen.
> Valid program names are:
>   arff.vector: : Generate Vectors from an ARFF file or directory
>   baumwelch: : Baum-Welch algorithm for unsupervised HMM training
>   canopy: : Canopy clustering
>   cat: : Print a file or resource as the logistic regression models would
> see it
>   cleansvd: : Cleanup and verification of SVD output
>   clusterdump: : Dump cluster output to text
>   clusterpp: : Groups Clustering Output In Clusters
>   cmdump: : Dump confusion matrix in HTML or text formats
>   cvb: : LDA via Collapsed Variation Bayes (0th deriv. approx)
>   cvb0_local: : LDA via Collapsed Variation Bayes, in memory locally.
>   dirichlet: : Dirichlet Clustering
>   eigencuts: : Eigencuts spectral clustering
>   evaluateFactorization: : compute RMSE and MAE of a rating matrix
> factorization against probes
>   fkmeans: : Fuzzy K-means clustering
>   fpg: : Frequent Pattern Growth
>   hmmpredict: : Generate random sequence of observations by given HMM
>   itemsimilarity: : Compute the item-item-similarities for item-based
> collaborative filtering
>   kmeans: : K-means clustering
>   lucene.vector: : Generate Vectors from a Lucene index
>   matrixdump: : Dump matrix in CSV format
>   matrixmult: : Take the product of two matrices
>   meanshift: : Mean Shift clustering
>   minhash: : Run Minhash clustering
>   parallelALS: : ALS-WR factorization of a rating matrix
>   recommendfactorized: : Compute recommendations using the factorization
> of a rating matrix
>   recommenditembased: : Compute recommendations using item-based
> collaborative filtering
>   regexconverter: : Convert text files on a per line basis based on
> regular expressions
>   rowid: : Map SequenceFile<Text,VectorWritable> to
> {SequenceFile<IntWritable,VectorWritable>, SequenceFile<IntWritable,Text>}
>   rowsimilarity: : Compute the pairwise similarities of the rows of a
> matrix
>   runAdaptiveLogistic: : Score new production data using a probably
> trained and validated AdaptivelogisticRegression model
>   runlogistic: : Run a logistic regression model against CSV data
>   seq2encoded: : Encoded Sparse Vector generation from Text sequence files
>   seq2sparse: : Sparse Vector generation from Text sequence files
>   seqdirectory: : Generate sequence files (of Text) from a directory
>   seqdumper: : Generic Sequence File dumper
>   seqmailarchives: : Creates SequenceFile from a directory containing
> gzipped mail archives
>   seqwiki: : Wikipedia xml dump to sequence file
>   spectralkmeans: : Spectral k-means clustering
>   split: : Split Input data into test and train sets
>   splitDataset: : split a rating dataset into training and probe parts
>   ssvd: : Stochastic SVD
>   svd: : Lanczos Singular Value Decomposition
>   testnb: : Test the Vector-based Bayes classifier
>   trainAdaptiveLogistic: : Train an AdaptivelogisticRegression model
>   trainlogistic: : Train a logistic regression using stochastic gradient
> descent
>   trainnb: : Train the Vector-based Bayes classifier
>   transpose: : Take the transpose of a matrix
>   validateAdaptiveLogistic: : Validate an AdaptivelogisticRegression model
> against hold-out data set
>   vecdist: : Compute the distances between a set of Vectors (or Cluster or
> Canopy, they must fit in memory) and a list of Vectors
>   vectordump: : Dump vectors from a sequence file to text
>   viterbi: : Viterbi decoding of hidden states from given output states
> sequence
>
>
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-----
THANKS AND REGARDS,
SYED ABDUL KATHER
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