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$ mahout | |
Running on hadoop, using /home/akm/hadoop-2.4.1/bin/hadoop and HADOOP_CONF_DIR= | |
MAHOUT-JOB: /home/akm/mahout/examples/target/mahout-examples-0.10.1-SNAPSHOT-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 directory | |
baumwelch: : Baum-Welch algorithm for unsupervised HMM training | |
buildforest: : Build the random forest classifier | |
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 | |
concatmatrices: : Concatenates 2 matrices of same cardinality into a single matrix | |
cvb: : LDA via Collapsed Variation Bayes (0th deriv. approx) | |
cvb0_local: : LDA via Collapsed Variation Bayes, in memory locally. | |
describe: : Describe the fields and target variable in a data set | |
evaluateFactorization: : compute RMSE and MAE of a rating matrix factorization against probes | |
fkmeans: : Fuzzy K-means clustering | |
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 | |
lucene2seq: : Generate Text SequenceFiles from a Lucene index | |
matrixdump: : Dump matrix in CSV format | |
matrixmult: : Take the product of two matrices | |
parallelALS: : ALS-WR factorization of a rating matrix | |
qualcluster: : Runs clustering experiments and summarizes results in a CSV | |
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 | |
resplit: : Splits a set of SequenceFiles into a number of equal splits | |
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 | |
streamingkmeans: : Streaming k-means clustering | |
svd: : Lanczos Singular Value Decomposition | |
testforest: : Test the random forest classifier | |
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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