tensor_predictors/tensorPredictors/R/kpir_ls.R

#' Per mode (axis) alternating least squares estimate
#'
#' @param sample.axis index of the sample mode, a.k.a. observation axis index
#'
#' @export
kpir.ls <- function(X, Fy, max.iter = 20L, sample.axis = 1L,
    eps = sqrt(.Machine$double.eps), center = TRUE, logger = NULL
) {
    ### Step 0: Setup/Initialization
    if (!is.array(Fy)) {
        # scalar response case (add new axis of size 1)
        dim(Fy) <- ifelse(seq_along(dim(X)) == sample.axis, dim(X)[sample.axis], 1L)
    }
    # Check dimensions and matching of axis (tensor order)
    stopifnot(exprs = {
        length(dim(X)) == length(dim(Fy))
        dim(X)[sample.axis] == dim(Fy)[sample.axis]
    })
    # warn about occurence of an axis without reduction
    if (any(dim(Fy)[-sample.axis] >= dim(X)[-sample.axis])) {
        warning("Degenerate case 'any(dim(Fy)[-sample.axis] >= dim(X)[-sample.axis])'")
    }

    # mode index sequence (exclude sample mode, a.k.a. observation axis)
    modes <- seq_along(dim(X))[-sample.axis]
    n <- dim(X)[sample.axis]                    # observation count (scalar)
    p <- dim(X)[-sample.axis]                   # predictor dimensions (vector)

    if (center) {
        # Means for X and Fy (a.k.a. sum elements over the sample axis)
        meanX <- apply(X, modes, mean, simplify = TRUE)
        meanFy <- apply(Fy, modes, mean, simplify = TRUE)
        # Center both X and Fy
        X <- sweep(X, modes, meanX)
        Fy <- sweep(Fy, modes, meanFy)
    } else {
        meanX <- meanFy <- NA
    }


    ### Step 1: initial per mode estimates
    alphas <- Map(function(mode, ncol) {
        La.svd(mcrossprod(X, mode = mode), ncol)$u
    }, modes, dim(Fy)[modes])


   ### Step 2: iterate per mode (axis) least squares estimates
    for (iter in seq_len(max.iter)) {
 
        # Invoke logger for previous iterate
        if (is.function(logger)) {
            logger("ls", iter - 1L, alphas)
        }
 
        # cyclic iterate over modes
        for (j in seq_along(modes)) {
            # least squares solution for `alpha_j | alpha_i, i != j`
            Z <- mlm(Fy, alphas[-j], modes = modes[-j])
            alphas[[j]] <- t(solve(
                mcrossprod(Z, Z, modes[j]), mcrossprod(Z, X, modes[j])
            ))
        }

        # TODO: add some kind of break condition
    }

    ### Step 3: Moment estimates for `Delta_i`
    # Residuals
    R <- X - mlm(Fy, alphas, modes = modes)
    # Moment estimates for `Delta_i`s
    Deltas <- Map(mcrossprod, list(R), mode = modes)
    Deltas <- Map(`*`, p / (n * prod(p)), Deltas)

    # Call logger with final results (including Deltas)
    if (is.function(logger)) {
        logger("ls", iter, alphas, Deltas)
    }

    list(alphas = structure(alphas, names = as.character(modes)),
         Deltas = structure(Deltas, names = as.character(modes)),
         meanX = meanX, meanFy = meanFy)
}