update runSTG

#' a larger lambda leads to fewer genes, default 1.5

#' @param n.runs integer, number of runs to run the model, default 5

#' @param python.use character string, the Python to use, default "/usr/bin/python"

#' @param source.code character string, the neural network source code, default "./STG_model.py"

#' @param return.model a logical value to indicate whether to return the actual model of STG

#' @param GPU which GPU to use, default '', using CPU

#'

#'

runSTG <- function(

  X, X.labels, lambda = 1.5, n.runs = 5, return.model = F,

  python.use = "/usr/bin/python", source.code = "./STG_model.py", GPU = ""

  python.use = "/usr/bin/python", GPU = ""

){

  # set Python

  use_python(python = python.use, required = T)

  source_python(file = source.code)

  source_python(file = paste(system.file(package="DAseq"), "DA_STG.py", sep = "/"))

  X.py <- r_to_py(as.matrix(X))

\title{Run STG}

\usage{

runSTG(X, X.labels, lambda = 1.5, n.runs = 5, return.model = F,

  python.use = "/usr/bin/python", source.code = "./STG_model.py",

  GPU = "")

  python.use = "/usr/bin/python", GPU = "")

}

\arguments{

\item{X}{matrix, normalized expression matrix of all cells in the dataset, genes are in rows,

\item{python.use}{character string, the Python to use, default "/usr/bin/python"}

\item{source.code}{character string, the neural network source code, default "./STG_model.py"}

\item{GPU}{which GPU to use, default '', using CPU}

}

\value{