--- a
+++ b/man/seq_encoding_label.Rd
@@ -0,0 +1,104 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/preprocess.R
+\name{seq_encoding_label}
+\alias{seq_encoding_label}
+\title{Encodes integer sequence for label classification.}
+\usage{
+seq_encoding_label(
+  sequence = NULL,
+  maxlen,
+  vocabulary,
+  start_ind,
+  ambiguous_nuc = "zero",
+  nuc_dist = NULL,
+  quality_vector = NULL,
+  use_coverage = FALSE,
+  max_cov = NULL,
+  cov_vector = NULL,
+  n_gram = NULL,
+  n_gram_stride = 1,
+  masked_lm = NULL,
+  char_sequence = NULL,
+  tokenizer = NULL,
+  adjust_start_ind = FALSE,
+  return_int = FALSE
+)
+}
+\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{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{masked_lm}{If not \code{NULL}, input and target are equal except some parts of the input are masked or random.
+Must be list with the following arguments:
+\itemize{
+\item \code{mask_rate}: Rate of input to mask (rate of input to replace with mask token).
+\item \code{random_rate}: Rate of input to set to random token.
+\item \code{identity_rate}: Rate of input where sample weights are applied but input and output are identical.
+\item \code{include_sw}: Whether to include sample weights.
+\item \code{block_len} (optional): Masked/random/identity regions appear in blocks of size \code{block_len}.
+}}
+
+\item{char_sequence}{A character string.}
+
+\item{tokenizer}{A keras tokenizer.}
+
+\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{return_int}{Whether to return integer encoding or one-hot encoding.}
+}
+\value{
+A list of 2 tensors.
+}
+\description{
+Returns encoding for integer or character sequence.
+}
+\examples{
+\dontshow{if (reticulate::py_module_available("tensorflow")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+# use integer sequence as input
+x <- seq_encoding_label(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")
+
+x[1,,] # 1,0,5,1,3
+
+x[2,,] # 5,1,3,4,
+
+# use character string as input
+x <- seq_encoding_label(maxlen = 5,
+                        vocabulary = c("a", "c", "g", "t"),
+                        start_ind = c(1,3),
+                        ambiguous_nuc = "equal",
+                        char_sequence = "ACTaaTNTNaZ")
+
+x[1,,] # actaa
+
+x[2,,] # taatn
+\dontshow{\}) # examplesIf}
+}