RVowpalWabbit (version 0.0.18)

vw: Run the Vowpal Wabbit fast out-of-core learner

Description

The vw function applies the Vowpal Wabbit on-line learner to a given data set and model.

Vowpal Wabbit is a project sponsored by Yahoo! Research and led by John Langford.

At present, this package provides a simple yet crude interface.

Usage

vw(args, quiet=TRUE)

Value

The vw returns a small data.frame with a number of summary statistics function returns a character string of a fixed length containing the requested digest of the supplied R object. For MD5, a string of length 32 is returned; for SHA-1, a string of length 40 is returned; for CRC32 a string of length 8.

Arguments

args

A character vector containing the same arguments one would use on the command-line with the standalone vw binary.

quiet

A boolean switch which, if set, suppresses most output to stdout.

Author

Dirk Eddelbuettel for the R interface; John Langford along with Daniel Hsu, Nikos Karampatziakis, Olivier Chapelle, Paul Mineiro, Matt Hoffman, Jake Hofman, Sudarshan Lamkhede, Shubham Chopra, Ariel Faigon, Lihong Li, Gordon Rios, and Alex Strehl for Vowpal Wabbit.

Details

Vowpal Wabbit is a very fast on-line machine learning application. Some documentation for it is provided via the upstream wiki referenced below.

References

https://github.com/VowpalWabbit/vowpal_wabbit/wiki

Examples

  ## also see demo(vw) from which this is a subset

  library(RVowpalWabbit)

  # Test 3: without -d, training only
  # {VW} train-sets/0002.dat    -f models/0002.model
  test3 <- c("-t", system.file("test", "train-sets", "0002.dat", package="RVowpalWabbit"),
             "-f", file.path(tempdir(), "0002.model"),
             "--cache_file", file.path(tempdir(), "0002.cache"))

  res <- vw(test3)
  res