[jediael@master mahout-distribution-0.9]$ mahout
Running on hadoop, using /mnt/jediael/hadoop-1.2.1/bin/hadoop and HADOOP_CONF_DIR=
MAHOUT-JOB: /mnt/jediael/mahout/mahout-distribution-0.9/examples/target/mahout-examples-0.9-job.jar
An example program must be given as the first argument.
Valid program names are:arff.vector: : Generate Vectors from an ARFF file or directorybaumwelch: : Baum-Welch algorithm for unsupervised HMM trainingcanopy: : Canopy clusteringcat: : Print a file or resource as the logistic regression models would see itcleansvd: : Cleanup and verification of SVD outputclusterdump: : Dump cluster output to textclusterpp: : Groups Clustering Output In Clusterscmdump: : Dump confusion matrix in HTML or text formatsconcatmatrices: : Concatenates 2 matrices of same cardinality into a single matrixcvb: : LDA via Collapsed Variation Bayes (0th deriv. approx)cvb0_local: : LDA via Collapsed Variation Bayes, in memory locally.evaluateFactorization: : compute RMSE and MAE of a rating matrix factorization against probesfkmeans: : Fuzzy K-means clusteringhmmpredict: : Generate random sequence of observations by given HMMitemsimilarity: : Compute the item-item-similarities for item-based collaborative filteringkmeans: : K-means clusteringlucene.vector: : Generate Vectors from a Lucene indexlucene2seq: : Generate Text SequenceFiles from a Lucene indexmatrixdump: : Dump matrix in CSV formatmatrixmult: : Take the product of two matricesparallelALS: : ALS-WR factorization of a rating matrixqualcluster: : Runs clustering experiments and summarizes results in a CSVrecommendfactorized: : Compute recommendations using the factorization of a rating matrixrecommenditembased: : Compute recommendations using item-based collaborative filteringregexconverter: : Convert text files on a per line basis based on regular expressionsresplit: : Splits a set of SequenceFiles into a number of equal splitsrowid: : Map SequenceFile<Text,VectorWritable> to {SequenceFile<IntWritable,VectorWritable>, SequenceFile<IntWritable,Text>}rowsimilarity: : Compute the pairwise similarities of the rows of a matrixrunAdaptiveLogistic: : Score new production data using a probably trained and validated AdaptivelogisticRegression modelrunlogistic: : Run a logistic regression model against CSV dataseq2encoded: : Encoded Sparse Vector generation from Text sequence filesseq2sparse: : Sparse Vector generation from Text sequence filesseqdirectory: : Generate sequence files (of Text) from a directoryseqdumper: : Generic Sequence File dumperseqmailarchives: : Creates SequenceFile from a directory containing gzipped mail archivesseqwiki: : Wikipedia xml dump to sequence filespectralkmeans: : Spectral k-means clusteringsplit: : Split Input data into test and train setssplitDataset: : split a rating dataset into training and probe partsssvd: : Stochastic SVDstreamingkmeans: : Streaming k-means clusteringsvd: : Lanczos Singular Value Decompositiontestnb: : Test the Vector-based Bayes classifiertrainAdaptiveLogistic: : Train an AdaptivelogisticRegression modeltrainlogistic: : Train a logistic regression using stochastic gradient descenttrainnb: : Train the Vector-based Bayes classifiertranspose: : Take the transpose of a matrixvalidateAdaptiveLogistic: : Validate an AdaptivelogisticRegression model against hold-out data setvecdist: : Compute the distances between a set of Vectors (or Cluster or Canopy, they must fit in memory) and a list of Vectorsvectordump: : Dump vectors from a sequence file to textviterbi: : Viterbi decoding of hidden states from given output states sequence
