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##' Train a model by word2vec.
##'
##' The word2vec tool takes a text corpus as input and produces the
##' word vectors as output. It first constructs a vocabulary from the
##' training text data and then learns vector representation of words.
##' The resulting word vector file can be used as features in many
##' natural language processing and machine learning applications.
##'
##' @title Train a model by word2vec.
##' @param train_file Path of the train file.
##' @param output_file Path of the output file.
##' @return A word2vec object.
##' @author Jian Li <\email{rweibo@@sina.com}>
##' @references \url{https://code.google.com/p/word2vec/}
##' @examples \dontrun{
##' word2vec(system.file("examples", "rfaq.txt", package = "tmcn.word2vec"))
##' }
word2vec <- function(train_file, output_file)
{
if (!file.exists(train_file)) stop("Can't find the trsin file!")
train_dir <- dirname(train_file)
if(missing(output_file)) {
output_file <- gsub(gsub("^.*\\.", "", basename(train_file)), "bin", basename(train_file))
output_file <- file.path(train_dir, output_file)
}
outfile_dir <- dirname(output_file)
if (!file.exists(outfile_dir)) dir.create(outfile_dir, recursive = TRUE)
train_file <- normalizePath(train_file, winslash = "/", mustWork = FALSE)
output_file <- normalizePath(output_file, winslash = "/", mustWork = FALSE)
# Whether to output binary, default is 1 means binary.
binary = 1
OUT <- .C("CWrapper_word2vec",
train_file = as.character(train_file),
output_file = as.character(output_file),
binary = as.character(binary))
class(OUT) <- "word2vec"
names(OUT)[2] <- "model_file"
cat(paste("The model was generated in '", dirname(output_file), "'!\n", sep = ""))
return(OUT)
}
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