Hi everyone,I downloaded the latest version of Mahout and did mvn install. When I try to run
fog, I get the following errors. Do I need to download and compile FPG separately? Looks like
somehow it has not been included in the list of valid programs.
13/11/19 17:49:19 WARN driver.MahoutDriver: Unable to add class: fpg13/11/19 17:49:19 WARN
driver.MahoutDriver: No fpg.props found on classpath, will use commandline arguments onlyUnknown
program 'fpg' chosen.Valid program names are: arff.vector: : Generate Vectors from an ARFF
file or directory baumwelch: : BaumWelch 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 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. evaluateFactorization: : compute
RMSE and MAE of a rating matrix factorization against probes fkmeans: : Fuzzy Kmeans clustering
hmmpredict: : Generate random sequence of observations by given HMM itemsimilarity: : Compute
the itemitemsimilarities for itembased collaborative filtering kmeans: : Kmeans 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: : ALSWR 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 itembased 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 kmeans 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 kmeans clustering svd: : Lanczos
Singular Value Decomposition testnb: : Test the Vectorbased Bayes classifier trainAdaptiveLogistic:
: Train an AdaptivelogisticRegression model trainlogistic: : Train a logistic regression
using stochastic gradient descent trainnb: : Train the Vectorbased Bayes classifier transpose:
: Take the transpose of a matrix validateAdaptiveLogistic: : Validate an AdaptivelogisticRegression
model against holdout 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
