54 lines
1.7 KiB
R
54 lines
1.7 KiB
R
#' Per mode (axis) alternating least squares estimate
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#'
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#' @param sample.mode index of the sample mode, a.k.a. observation axis index
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#'
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#' @export
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kpir.ls <- function(X, Fy, max.iter = 20L, sample.mode = 1L,
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eps = .Machine$double.eps, logger = NULL
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) {
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# Check if X and Fy have same number of observations
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if (!is.array(Fy)) {
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# scalar response case (add new axis of size 1)
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dim(Fy) <- local({
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dims <- rep(1, length(dim(X)))
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dims[sample.mode] <- length(Fy)
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dims
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})
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} else {
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stopifnot(dim(X)[sample.mode] == dim(Fy)[sample.mode])
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}
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# and check shape
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stopifnot(length(X) == length(Fy))
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# mode index sequence (exclude sample mode, a.k.a. observation axis)
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modes <- seq_along(dim(X))[-sample.mode]
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### Step 1: initial per mode estimates
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alphas <- Map(function(mode, ncol) {
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La.svd(mcrossprod(X, mode), ncol)$u
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}, modes, dim(Fy)[modes])
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# Call history callback (logger) before the first iteration
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if (is.function(logger)) { logger(0L, alphas) }
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### Step 2: iterate per mode (axis) least squares estimates
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for (iter in seq_len(max.iter)) {
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# cyclic iterate over modes
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for (j in seq_along(modes)) {
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# least squares solution for `alpha_j | alpha_i, i != j`
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Z <- mlm(Fy, alphas[-j], modes = modes[-j])
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alphas[[j]] <- t(solve(mcrossprod(Z, j), tcrossprod(mat(Z, j), mat(X, j))))
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# TODO: alphas[[j]] <- t(solve(mcrossprod(Z, j), mcrossprod(Z, X, j)))
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}
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# Call logger (invoke history callback)
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if (is.function(logger)) { logger(iter, alphas) }
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# TODO: add some kind of break condition
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}
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}
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