tmp-save/crossprodSelf.R

In privefl/bigstatsr: Statistical Tools for Filebacked Big Matrices

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#' Crossprod
#'
#' Compute \eqn{X.row^T X.row} for a Filebacked Big Matrix `X`
#' after applying a particular scaling to it.
#'
#' This algorithm is not really memory efficient. I'm planning on fixing this,
#' if it is really needed.
#'
#' @inheritParams bigstatsr-package
#'
#' @return A matrix, \eqn{X.row^T X.row}, with the following two attributes:
#' - a numeric vector `mean` of column scaling,
#' - a numeric vector `sd` of column scaling.
#' @export
#' @seealso [crossprod]
#'
#' @example examples/example-crossprodSelf.R
#'
big_crossprodSelf <- function(X,
                              fun.scaling,
                              ind.row = rows_along(X),
                              ind.col = cols_along(X),
                              block.size = block_size(nrow(X))) {

  check_args()

  m <- length(ind.col)
  K <- matrix(NA_real_, m, m)

  # means and sds of each column
  ms <- fun.scaling(X, ind.row = ind.row, ind.col = ind.col)

  intervals <- CutBySize(m, block.size)
  nb.block <- nrow(intervals)

  for (j in 1:nb.block) {
    ind1 <- seq2(intervals[j, ])
    tmp1 <- scaling(X[ind.row, ind.col[ind1]], ms$mean[ind1], ms$sd[ind1])
    for (i in 1:j) {
      ind2 <- seq2(intervals[i, ])
      tmp2 <- scaling(X[ind.row, ind.col[ind2]], ms$mean[ind2], ms$sd[ind2])

      K[ind2, ind1] <- crossprod(tmp2, tmp1)
    }
  }

  # Complete the lower part of the symmetric matrix
  structure(complete2(K), mean = ms$mean, sd = ms$sd)
}

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