Nothing
dPLS <- function(X, M=NULL, pseudocount=.Machine$double.eps,
initV=NULL, fixV=FALSE, Ter_V=1e-10,
L1_V=1e-10, L2_V=1e-10, eta=1e+10, J = 3,
thr = 1e-10, num.iter = 100,
viz = FALSE, figdir = NULL, verbose = FALSE){
# Argument check
.checkdPLS(X, M, pseudocount, initV, fixV,
Ter_V, L1_V, L2_V, eta, J, thr, num.iter, viz, figdir, verbose)
# Initialization of U, V
int <- .initdPLS(X, M, pseudocount, fixV, initV, J, thr, verbose)
X <- int$X
M <- int$M
pM <- int$pM
M_NA <- int$M_NA
fixV <- int$fixV
U <- int$U
V <- int$V
RecError <- int$RecError
TrainRecError <- int$TrainRecError
TestRecError <- int$TestRecError
RelChange <- int$RelChange
iter <- 1
while ((RecError[iter] > thr) && (iter <= num.iter)) {
# Before Update W, H_k
X_bar <- lapply(seq_along(X), function(x){
.recMatrix(U[[x]], V[[x]])
})
pre_Error <- sqrt(sum(unlist(lapply(seq_along(X), function(x){
.recError(X[[x]], X_bar[[x]], notsqrt=TRUE)
}))))
# Update U, V
V <- .updateV_dPLS(X, pM, V, fixV, Ter_V, L1_V, L2_V, eta, iter)
U <- lapply(seq_along(X), function(x){
X[[x]] %*% V[[x]]
})
# After Update U, V
iter <- iter + 1
X_bar <- lapply(seq_along(X), function(x){
.recMatrix(U[[x]], V[[x]])
})
RecError[iter] <- sqrt(sum(unlist(lapply(seq_along(X), function(x){
.recError(X[[x]], X_bar[[x]], notsqrt=TRUE)
}))))
TrainRecError[iter] <- sqrt(sum(unlist(lapply(seq_along(X), function(x){
.recError((1-M_NA[[x]]+M[[x]]) * X[[x]], (1-M_NA[[x]]+M[[x]]) * X_bar[[x]], notsqrt=TRUE)
}))))
TestRecError[iter] <- sqrt(sum(unlist(lapply(seq_along(X), function(x){
.recError((M_NA[[x]]-M[[x]]) * X[[x]], (M_NA[[x]]-M[[x]]) * X_bar[[x]], notsqrt=TRUE)
}))))
RelChange[iter] <- abs(pre_Error - RecError[iter]) / RecError[iter]
# Visualization
if (viz && !is.null(figdir)) {
png(filename = paste0(figdir, "/", iter-1, ".png"))
lapply(X_bar, image.plot)
dev.off()
}
if (viz && is.null(figdir)) {
lapply(X_bar, image.plot)
}
# Verbose Message
if (verbose) {
cat(paste0(iter-1, " / ", num.iter, " |Previous Error - Error| / Error = ",
RelChange[iter], "\n"))
}
# Exception Handling
if (is.nan(RelChange[iter])) {
stop("NaN is generated. Please run again or change the parameters.\n")
}
}
# Visualization
if (viz && !is.null(figdir)) {
png(filename = paste0(figdir, "/finish.png"))
lapply(X_bar, image.plot)
dev.off()
png(filename = paste0(figdir, "/original.png"))
lapply(X, image.plot)
dev.off()
}
if (viz && is.null(figdir)) {
lapply(X_bar, image.plot)
}
# Output
names(RecError) <- c("offset", seq_len(iter-1))
names(TrainRecError) <- c("offset", seq_len(iter-1))
names(TestRecError) <- c("offset", seq_len(iter-1))
names(RelChange) <- c("offset", seq_len(iter-1))
list(U = U, V = V, RecError = RecError,
TrainRecError = TrainRecError,
TestRecError = TestRecError,
RelChange = RelChange)
}
.checkdPLS <- function(X, M, pseudocount, initV, fixV,
Ter_V, L1_V, L2_V, eta, J, thr, num.iter, viz, figdir, verbose){
if(length(X) < 2){
stop("input list X must have at least two datasets!")
}
if(!is.null(M)){
dimX <- as.vector(unlist(lapply(X, function(x){dim(x)})))
dimM <- as.vector(unlist(lapply(M, function(x){dim(x)})))
if(!identical(dimX, dimM)){
stop("Please specify the dimensions of X and M are same")
}
lapply(seq(length(X)), function(i){
.checkZeroNA(X[[i]], M[[i]], type="matrix")
})
}
stopifnot(is.numeric(pseudocount))
if(!is.null(initV)){
if(!identical(ncol(X), nrow(initV))){
stop("Please specify ncol(X) and nrow(initV) are same")
}
}
stopifnot(is.logical(fixV))
if(Ter_V < 0){
stop("Please specify the Ter_V that larger than 0")
}
if(L1_V < 0){
stop("Please specify the L1_V that larger than 0")
}
if(L2_V < 0){
stop("Please specify the L2_V that larger than 0")
}
if(eta < 0){
stop("Please specify the eta that larger than 0")
}
stopifnot(is.numeric(J))
stopifnot(is.numeric(thr))
stopifnot(is.numeric(num.iter))
stopifnot(is.logical(viz))
if(!is.character(figdir) && !is.null(figdir)){
stop("Please specify the figdir as a string or NULL")
}
stopifnot(is.logical(verbose))
}
.initdPLS <- function(X, M, pseudocount, fixV, initV, J, thr, verbose){
if(is.logical(fixV)){
fixV <- rep(fixV, length=length(X))
}
# NA mask
M_NA <- list()
length(M_NA) <- length(X)
for(i in seq_along(X)){
M_NA[[i]] <- X[[i]]
M_NA[[i]][] <- 1
M_NA[[i]][which(is.na(X[[i]]))] <- 0
}
if(is.null(M)){
M <- M_NA
}
pM <- M
# Pseudo count
for(i in seq_along(X)){
X[[i]][which(is.na(X[[i]]))] <- pseudocount
X[[i]][which(X[[i]] == 0)] <- pseudocount
pM[[i]][which(pM[[i]] == 0)] <- pseudocount
}
if(is.null(initV)){
V <- lapply(seq_along(X), function(x){
tmp <- matrix(runif(ncol(X[[x]])*J),
nrow=ncol(X[[x]]), ncol=J)
.columnNorm(tmp)
})
}else{
V <- initV
}
U <- lapply(seq_along(X), function(x){
X[[x]] %*% V[[x]]
})
RecError = c()
TrainRecError = c()
TestRecError = c()
RelChange = c()
RecError[1] <- thr * 10
TrainRecError[1] <- thr * 10
TestRecError[1] <- thr * 10
RelChange[1] <- thr * 10
if (verbose) {
cat("Iterative step is running...\n")
}
list(X=X, M=M, pM=pM, M_NA=M_NA, fixV=fixV,
U=U, V=V, RecError=RecError,
TrainRecError=TrainRecError,
TestRecError=TestRecError, RelChange=RelChange)
}
.updateV_dPLS <- function(X, pM, V, fixV, Ter_V, L1_V, L2_V, eta, iter){
stepSize <- eta / iter
for(i in seq_along(V)){
if(!fixV[i]){
grad <- lapply(setdiff(seq_along(V), i), function(j){
t(X[[i]] * pM[[i]]) %*% (X[[j]] * pM[[j]]) %*% V[[j]] %*% diag(ncol(V[[j]]):1)
})
grad <- do.call("+", grad)
L1Term <- L1_V
L2Term <- L2_V * V[[i]]
TerTerm <- Ter_V * (3 * V[[i]]^5 - 4 * V[[i]]^3 + V[[i]])
V[[i]] <- .scaleQR(V[[i]] + stepSize * grad - L1Term - L2Term - TerTerm)
}
}
V
}
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