--- a
+++ b/man/seq_encoding_lm.Rd
@@ -0,0 +1,125 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/preprocess.R
+\name{seq_encoding_lm}
+\alias{seq_encoding_lm}
+\title{Encodes integer sequence for language model}
+\usage{
+seq_encoding_lm(
+  sequence = NULL,
+  maxlen,
+  vocabulary,
+  start_ind,
+  ambiguous_nuc = "zero",
+  nuc_dist = NULL,
+  quality_vector = NULL,
+  return_int = FALSE,
+  target_len = 1,
+  use_coverage = FALSE,
+  max_cov = NULL,
+  cov_vector = NULL,
+  n_gram = NULL,
+  n_gram_stride = 1,
+  output_format = "target_right",
+  char_sequence = NULL,
+  adjust_start_ind = FALSE,
+  tokenizer = NULL
+)
+}
+\arguments{
+\item{sequence}{Sequence of integers.}
+
+\item{maxlen}{Length of predictor sequence.}
+
+\item{vocabulary}{Vector of allowed characters. Characters outside vocabulary get encoded as specified in \code{ambiguous_nuc}.}
+
+\item{start_ind}{Start positions of samples in \code{sequence}.}
+
+\item{ambiguous_nuc}{How to handle nucleotides outside vocabulary, either \code{"zero"}, \code{"empirical"} or \code{"equal"}.
+See \code{\link{train_model}}. Note that \code{"discard"} option is not available for this function.}
+
+\item{nuc_dist}{Nucleotide distribution.}
+
+\item{quality_vector}{Vector of quality probabilities.}
+
+\item{return_int}{Whether to return integer encoding or one-hot encoding.}
+
+\item{target_len}{Number of nucleotides to predict at once for language model.}
+
+\item{use_coverage}{Integer or \code{NULL}. If not \code{NULL}, use coverage as encoding rather than one-hot encoding and normalize.
+Coverage information must be contained in fasta header: there must be a string \code{"cov_n"} in the header, where \code{n} is some integer.}
+
+\item{max_cov}{Biggest coverage value. Only applies if \code{use_coverage = TRUE}.}
+
+\item{cov_vector}{Vector of coverage values associated to the input.}
+
+\item{n_gram}{Integer, encode target not nucleotide wise but combine n nucleotides at once. For example for \verb{n=2, "AA" ->  (1, 0,..., 0),}
+\verb{"AC" ->  (0, 1, 0,..., 0), "TT" -> (0,..., 0, 1)}, where the one-hot vectors have length \code{length(vocabulary)^n}.}
+
+\item{n_gram_stride}{Step size for n-gram encoding. For AACCGGTT with \code{n_gram = 4} and \code{n_gram_stride = 2}, generator encodes
+\verb{(AACC), (CCGG), (GGTT)}; for \code{n_gram_stride = 4} generator encodes \verb{(AACC), (GGTT)}.}
+
+\item{output_format}{Determines shape of output tensor for language model.
+Either \code{"target_right"}, \code{"target_middle_lstm"}, \code{"target_middle_cnn"} or \code{"wavenet"}.
+Assume a sequence \code{"AACCGTA"}. Output correspond as follows
+\itemize{
+\item \verb{"target_right": X = "AACCGT", Y = "A"}
+\item \verb{"target_middle_lstm": X = (X_1 = "AAC", X_2 = "ATG"), Y = "C"} (note reversed order of X_2)
+\item \verb{"target_middle_cnn": X = "AACGTA", Y = "C"}
+\item \verb{"wavenet": X = "AACCGT", Y = "ACCGTA"}
+}}
+
+\item{char_sequence}{A character string.}
+
+\item{adjust_start_ind}{Whether to shift values in \code{start_ind} to start at 1: for example (5,11,25) becomes (1,7,21).}
+
+\item{tokenizer}{A keras tokenizer.}
+}
+\value{
+A list of 2 tensors.
+}
+\description{
+Helper function for \code{\link{generator_fasta_lm}}.
+Encodes integer sequence to input/target list according to \code{output_format} argument.
+}
+\examples{
+\dontshow{if (reticulate::py_module_available("tensorflow")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+# use integer sequence as input 
+
+z <- seq_encoding_lm(sequence = c(1,0,5,1,3,4,3,1,4,1,2),
+maxlen = 5,
+vocabulary = c("a", "c", "g", "t"),
+start_ind = c(1,3),
+ambiguous_nuc = "equal",
+target_len = 1,
+output_format = "target_right")
+
+x <- z[[1]]
+y <- z[[2]]
+
+x[1,,] # 1,0,5,1,3
+y[1,] # 4
+
+x[2,,] # 5,1,3,4,
+y[2,] # 1
+
+# use character string as input
+z <- seq_encoding_lm(sequence = NULL,
+maxlen = 5,
+vocabulary = c("a", "c", "g", "t"),
+start_ind = c(1,3),
+ambiguous_nuc = "zero",
+target_len = 1,
+output_format = "target_right",
+char_sequence = "ACTaaTNTNaZ")
+
+
+x <- z[[1]]
+y <- z[[2]]
+
+x[1,,] # actaa
+y[1,] # t
+
+x[2,,] # taatn
+y[2,] # t
+\dontshow{\}) # examplesIf}
+}