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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