La Consejería de Empleo de la Función General de la Comunidad Autónoma de Ordenación Provincia de la Audiencia Profesional

library(mxnet)

batch.size     <- 32
seq.len        <- 64
num.hidden     <- 128
num.embed      <- 8
num.lstm.layer <- 1
num.round      <- 1
learning.rate  <- 0.1
wd             <- 0.00001
clip_gradient  <- 1
update.period  <- 1

make.data <- function(dir.boe, seq.len = 32,
  max.vocab=10000, dic = NULL) {
  text <- lapply(dir(dir.boe), readLines)
  text <- lapply(text, paste, collapse = "\n")
  text <- paste(text, collapse = "\n")

  char.lst <- strsplit(text, '')[[1]]
  chars <- unique(char.lst)

  num.seq  <- floor(length(char.lst) / seq.len)
  char.lst <- char.lst[1:(num.seq * seq.len)]
  data <- matrix(match(char.lst, chars) - 1, seq.len, num.seq)

  dic <- as.list(1:length(chars))
  names(dic) <- chars

  lookup.table <- as.list(chars)

  return (list(data = data, dic = dic,
    lookup.table = lookup.table))
}


ret <- make.data(".", seq.len=seq.len)

X   <- ret$data
dic <- ret$dic
lookup.table <- ret$lookup.table

vocab <- length(dic)

train.val.fraction <- 0.9
train.cols <- floor(ncol(X) * train.val.fraction)

drop.tail <- function(x, batch.size) {
  nstep <- floor(ncol(x) / batch.size)
  x[, 1:(nstep * batch.size)]
}

get.label <- function(X)
  matrix(c(X[-1], X[1]), nrow(X), ncol(X))

X.train.data   <- X[, 1:train.cols]
X.train.data   <- drop.tail(X.train.data, batch.size)
X.train.label  <- get.label(X.train.data)
X.train        <- list(data=X.train.data, label=X.train.label)

X.val.data     <- X[, -(1:train.cols)]
X.val.data     <- drop.tail(X.val.data, batch.size)
X.val.label    <- get.label(X.val.data)
X.val          <- list(data=X.val.data, label=X.val.label)


model <- mx.lstm(X.train, X.val,
    ctx=mx.cpu(),
    num.round=num.round,
    update.period=update.period,
    num.lstm.layer=num.lstm.layer,
    seq.len=seq.len,
    num.hidden=num.hidden,
    num.embed=num.embed,
    num.label=vocab,
    batch.size=batch.size,
    input.size=vocab,
    initializer=mx.init.uniform(0.1),
    learning.rate=learning.rate,
    wd=wd,
    clip_gradient=clip_gradient)


get.sample <- function(n, start = "<", random.sample = TRUE){

  make.output <- function(prob, sample = FALSE) {
    prob <- as.numeric(as.array(prob))
    if (!sample)
      return(which.max(as.array(prob)))
    sample(1:length(prob), 1, prob = prob^2)
  }

  infer.model <- mx.lstm.inference(
      num.lstm.layer=num.lstm.layer,
      input.size=vocab,
      num.hidden=num.hidden,
      num.embed=num.embed,
      num.label=vocab,
      arg.params=model$arg.params,
      ctx=mx.cpu())

  out <- start
  last.id <- dic[[start]]

  for (i in 1:(n-1)) {
    ret <- mx.lstm.forward(infer.model, last.id - 1, FALSE)
    infer.model <- ret$model
    last.id <- make.output(ret$prob, random.sample)
    out <- paste0(out, lookup.table[[last.id]])
  }
  out
}

cat(get.sample(1000, start = "A", random.sample = T))