[hxsyl@CentOSMaster hadoop-2.6.4]$ mahoutMAHOUT_LOCAL is not set; adding HADOOP_CONF_DIR to classpath.Running on hadoop, using HADOOP_HOME=/home/hxsyl/Spark_Relvant/hadoop-2.6.4HADOOP_CONF_DIR=/home/hxsyl/Spark_Relvant/hadoop-2.6.4/etc/hadoopMAHOUT-JOB: /home/hxsyl/Spark_Relvant/mahout-distribution-0.6/mahout-examples-0.6-job.jarAn example program must be given as the first argument.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  lda: : Latent Dirchlet Allocation  ldatopics: : LDA Print Topics  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  pagerank: : compute the PageRank of a graph  parallelALS: : ALS-WR factorization of a rating matrix  prepare20newsgroups: : Reformat 20 newsgroups data  randomwalkwithrestart: : compute all other vertices‘ proximity to a source vertex in a graph  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  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  testclassifier: : Test the text based Bayes Classifier  testnb: : Test the Vector-based Bayes classifier  trainAdaptiveLogistic: : Train an AdaptivelogisticRegression model  trainclassifier: : Train the text based Bayes Classifier  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  wikipediaDataSetCreator: : Splits data set of wikipedia wrt feature like country  wikipediaXMLSplitter: : Reads wikipedia data and creates ch