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
command-line arguments onlyUnknown program 'fpg' 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
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 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 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
