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Add to findThresold the option to specify a seed #192

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Add to findThresold the option to specify a seed #192

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22 changes: 13 additions & 9 deletions R/DistToNearest.R
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Original file line number Diff line number Diff line change
Expand Up @@ -1224,6 +1224,7 @@ distToNearest <- function(db, sequenceColumn="junction", vCallColumn="v_call", j
#' @param spc specificity required. Applies only when \code{method="gmm"} and \code{cutoff="user"}.
#'
#' @param progress if \code{TRUE} print a progress bar.
#' @param seed numeric value to set the random seed for reproducibility of the fitting procedure.
#' @return
#' \itemize{
#' \item \code{"gmm"} method: Returns a \link{GmmThreshold} object including the
Expand Down Expand Up @@ -1273,8 +1274,9 @@ distToNearest <- function(db, sequenceColumn="junction", vCallColumn="v_call", j
#' jCallColumn="j_call", model="ham", normalize="len", nproc=1)
#'
#' # Find threshold using the "gmm" method with user defined specificity
#' # Setting a seed value just for reproducibility of the example results
#' output <- findThreshold(db$dist_nearest, method="gmm", model="gamma-gamma",
#' cutoff="user", spc=0.99)
#' cutoff="user", spc=0.99, seed=234)
#' plot(output, binwidth=0.02, title=paste0(output@model, " loglk=", output@loglk))
#' print(output)
#' }
Expand All @@ -1283,7 +1285,7 @@ findThreshold <- function (distances, method=c("density", "gmm"),
edge=0.9, cross=NULL, subsample=NULL,
model=c("gamma-gamma", "gamma-norm", "norm-gamma", "norm-norm"),
cutoff=c("optimal", "intersect", "user"), sen=NULL, spc=NULL,
progress=FALSE){
progress=FALSE, seed=NULL) {
# Check arguments
method <- match.arg(method)
model <- match.arg(model)
Expand All @@ -1305,11 +1307,11 @@ findThreshold <- function (distances, method=c("density", "gmm"),
output <- NA
} else {
output <- gmmFit(ent=distances, edge=edge, cross=cross, model=model, cutoff=cutoff,
sen=sen, spc=spc, progress=progress)
sen=sen, spc=spc, progress=progress, seed=seed)
}
} else {
output <- gmmFit(ent=distances, edge=edge, cross=cross, model=model, cutoff=cutoff,
sen=sen, spc=spc, progress=progress)
sen=sen, spc=spc, progress=progress, seed=seed)
}
} else if (method == "density") {
output <- smoothValley(distances)
Expand Down Expand Up @@ -1456,7 +1458,7 @@ findThreshold <- function (distances, method=c("density", "gmm"),
# output <- findThreshold(db$dist_nearest, method="gmm", edge=0.9)
# # or
# output <- gmmFit(db$dist_nearest, edge=0.9)
gmmFit <- function(ent, edge=0.9, cross=NULL, model, cutoff, sen, spc, progress=FALSE) {
gmmFit <- function(ent, edge=0.9, cross=NULL, model, cutoff, sen, spc, progress=FALSE, seed=NULL) {

#************* Filter Unknown Data *************#
ent <- ent[!is.na(ent) & !is.nan(ent) & !is.infinite(ent)]
Expand Down Expand Up @@ -1491,7 +1493,7 @@ gmmFit <- function(ent, edge=0.9, cross=NULL, model, cutoff, sen, spc, progress=
}

#************* set rand seed *************#
set.seed(NULL)
set.seed(seed)

#************* define Number of Gaussians *************#
num_G <- 2
Expand Down Expand Up @@ -1628,7 +1630,7 @@ gmmFit <- function(ent, edge=0.9, cross=NULL, model, cutoff, sen, spc, progress=
sigma.gmm <- c(sigma[1], sigma[2])

fit_results <- rocSpace(ent=ent, omega.gmm=omega.gmm , mu.gmm=mu.gmm, sigma.gmm=sigma.gmm,
model=model, cutoff=cutoff, sen=sen, spc=spc, progress=progress)
model=model, cutoff=cutoff, sen=sen, spc=spc, progress=progress, seed=seed)
results <- new("GmmThreshold",
x=ent,
model=model,
Expand All @@ -1653,7 +1655,7 @@ gmmFit <- function(ent, edge=0.9, cross=NULL, model, cutoff, sen, spc, progress=
}


rocSpace <- function(ent, omega.gmm, mu.gmm, sigma.gmm, model, cutoff, sen, spc, progress=FALSE) {
rocSpace <- function(ent, omega.gmm, mu.gmm, sigma.gmm, model, cutoff, sen, spc, progress=FALSE, seed=NULL) {
func <- model
bits <- strsplit(func,'-')[[1]]

Expand Down Expand Up @@ -1682,7 +1684,9 @@ rocSpace <- function(ent, omega.gmm, mu.gmm, sigma.gmm, model, cutoff, sen, spc,
gmmfunc2.1 <- func2.1
gmmfunc2.2 <- func2.2

set.seed(NULL)
# Resets the RNG when no seed provided (original random behavior). When a seed
# is set, inherit the RNG state from gmmFit.
if (is.null(seed)) { set.seed(NULL) }
# options(warn=-1)
LOG_LIK <- 0
fit_found <- FALSE
Expand Down
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