49 lines
1.6 KiB
R
49 lines
1.6 KiB
R
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#' Compute get gradient of `L(V)` given a dataset `X`.
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#'
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#' @param X Data matrix.
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#' @param Y Responce.
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#' @param V Position to compute the gradient at, aka point on Stiefl manifold.
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#' @param h Bandwidth
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#' @param loss.out Iff \code{TRUE} loss will be written to parent environment.
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#' @param loss.only Boolean to only compute the loss, of \code{TRUE} a single
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#' value loss is returned and \code{envir} is ignored.
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#' @param persistent Determines if data indices and dependent calculations shall
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#' be reused from the parent environment. ATTENTION: Do NOT set this flag, only
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#' intended for internal usage by carefully aligned functions!
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#' @keywords internal
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#' @export
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grad <- function(X, Y, V, h,
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loss.out = FALSE,
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loss.only = FALSE,
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persistent = FALSE) {
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# Get number of samples and dimension.
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n <- nrow(X)
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p <- ncol(X)
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if (!persistent) {
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# Compute lookup indexes for symmetrie, lower/upper
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# triangular parts and vectorization.
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pair.index <- elem.pairs(seq(n))
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i <- pair.index[1, ] # `i` indices of `(i, j)` pairs
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j <- pair.index[2, ] # `j` indices of `(i, j)` pairs
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# Index of vectorized matrix, for lower and upper triangular part.
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lower <- ((i - 1) * n) + j
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upper <- ((j - 1) * n) + i
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# Create all pairewise differences of rows of `X`.
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X_diff <- X[i, , drop = F] - X[j, , drop = F]
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}
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out <- .Call("grad_c", PACKAGE = "CVE",
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X, X_diff, as.double(Y), V, as.double(h));
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if (loss.only) {
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return(out$loss)
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}
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if (loss.out) {
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loss <<- out$loss
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}
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return(out$G)
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}
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