Nothing
unmaskAndGetSampleBatch <- function(listOfMaskedVectorsToBeUnmasked,
listOfNoisefiles,
mu, s, rho_X,
cores = 1, size,
verbose = -1,
onlyUnmasked = FALSE) {
numberOfItemsToCheck <- 6
lengths <- rep(NA,numberOfItemsToCheck)
lengths[1] <- length(listOfMaskedVectorsToBeUnmasked)
lengths[2] <- length(listOfNoisefiles)
if(!(missing(mu))) {
lengths[3] <- length(mu)
}
if(!(missing(s))) {
lengths[4] <- length(s)
}
if(!(missing(rho_X))) {
lengths[5] <- nrow(rho_X)
lengths[6] <- ncol(rho_X)
}
lengths <- lengths[is.na(lengths) == FALSE]
if(length(unique(lengths)) != 1) {
print("lengths of arguments do not match")
return(NA)
}
unmaskedOutputs <- lapply(1:lengths[1], FUN = function(i) {
return(unmask(listOfMaskedVectorsToBeUnmasked[[i]],
listOfNoisefiles[[i]]))
})
if(onlyUnmasked) {
return(unmaskedOutputs)
}
unmaskedVectors <- lapply(1:lengths[1], FUN = function(i) {
return((unmaskedOutputs[[i]])$unmaskedVariable)
})
# note unmaskedVectors and unmaskedVariables are being conflated here
meansOfNoises <- lapply(1:lengths[1], FUN = function(i) {
return((unmaskedOutputs[[i]])$meanOfNoise)
})
meansOfSquaredNoises <- lapply(1:lengths[1], FUN = function(i) {
return((unmaskedOutputs[[i]])$meanOfSquaredNoise)
})
# sentinel <- "nullString"
# for(i in 1:lengths[1]) {
# if(mu[[i]] == sentinel) {
# # user did not supply an argument for this
# # use default value as in mask
# mu[[i]] <-
# mean(listOfMaskedVectorsToBeUnmasked[[i]])/meansOfNoises[[i]]
# }
# }
#
# for(i in 1:lengths[1]) {
# if(s[[i]] == sentinel) {
# # user did not supply an argument for this
# # use default value as in mask
# s[[i]] <-
# sqrt((mean(listOfMaskedVectorsToBeUnmasked[[i]]^2)
# -(meansOfSquaredNoises[[i]])*
# mean(listOfMaskedVectorsToBeUnmasked[[i]])^2/
# (meansOfNoises[[i]])^2)/
# (meansOfSquaredNoises[[i]]))
# }
# }
if(missing(mu)) {
mu <- lapply(1:lengths[1], FUN = function(i) {
mean(listOfMaskedVectorsToBeUnmasked[[i]])/meansOfNoises[[i]]
})}
if(missing(s)) {
s <- lapply(1:lengths[1], FUN = function(i) {
sqrt((mean(listOfMaskedVectorsToBeUnmasked[[i]]^2)-(meansOfSquaredNoises[[i]])*
mean(listOfMaskedVectorsToBeUnmasked[[i]])^2/(meansOfNoises[[i]])^2)/
(meansOfSquaredNoises[[i]]))
})}
if(missing(rho_X)) {
rho_X <- matrix(1,lengths[1],lengths[1])
for(i in 1:lengths[1]) {
for(j in 1:lengths[1]) {
if(i != j) {
rho_X[i,j] <- (cov(listOfMaskedVectorsToBeUnmasked[[i]],listOfMaskedVectorsToBeUnmasked[[j]])/((meansOfNoises[[i]])*(meansOfNoises[[j]])))/(s[[i]]*s[[j]])
rho_X[j,i] <- rho_X[i,j]
}
}
}
}
getSampleOutputs <- getSampleBasedOnUnmaskedData(meansOfNoises,
meansOfSquaredNoises,
listOfMaskedVectorsToBeUnmasked,
unmaskedVectors,
mu, s, rho_X,
cores, size, verbose)
return(list(unmaskedOutputs = unmaskedOutputs,
getSampleOutputs = getSampleOutputs))
}
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