Fixed repmat
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2 changed files with 15 additions and 3 deletions
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R/repmat.R
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R/repmat.R
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@ -6,13 +6,23 @@
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#' @param n either a scalar with the number of replications in both rows and columns or a 2-length vector with individual repetitions.
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#' @param n either a scalar with the number of replications in both rows and columns or a 2-length vector with individual repetitions.
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#' @return matrix replicated over `ncol(mx) * n` columns and `nrow(mx) * n` rows
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#' @return matrix replicated over `ncol(mx) * n` columns and `nrow(mx) * n` rows
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#' @note The Matlab implementation of this function accepts `n` with length > 2.
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#' @note The Matlab implementation of this function accepts `n` with length > 2.
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#'
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#' It should also be noted that a concatenated vector in R, e.g. `c(5, 2)`, becomes a column vector when coerced to matrix, even though it may look like a row vector at first glance. This is important to keep in mind when considering the expected output of this function. Vectors in R make sense to be seen as column vectors, given R's Statistics-oriented paradigm where variables are usually disposed as columns in a dataset.
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#' @export
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#' @export
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repmat <- function (mx, n) {
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repmat <- function (mx, n) {
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# Validation
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if (length(n) > 2) warning("Extra dimensions of n ignored")
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if (length(n) > 2) warning("Extra dimensions of n ignored")
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if (length(n) == 1) n <- rep(n, 2)
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if (length(n) == 1) n <- rep(n, 2)
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out <- mx_cols <- rep(mx, n[1])
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if (class(mx) != "matrix") mx <- as.matrix(mx)
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if (n[2] > 1) {
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for (i in seq(n[2] - 1)) out <- rbind(out, mx_cols)
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# Replicating cols
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out <- mx_col <- matrix(rep(mx, n[2]), nrow(mx))
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# Replicating rows
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if (n[1] > 1) {
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for (i in seq(n[1] - 1)) out <- rbind(out, mx_col)
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}
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}
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# Output
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return(unname(as.matrix(out)))
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return(unname(as.matrix(out)))
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}
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}
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@ -23,4 +23,6 @@ function on Matlab
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}
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}
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\note{
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\note{
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The Matlab implementation of this function accepts `n` with length > 2.
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The Matlab implementation of this function accepts `n` with length > 2.
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It should also be noted that a concatenated vector in R, e.g. `c(5, 2)`, becomes a column vector when coerced to matrix, even though it may look like a row vector at first glance. This is important to keep in mind when considering the expected output of this function. Vectors in R make sense to be seen as column vectors, given R's Statistics-oriented paradigm where variables are usually disposed as columns in a dataset.
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}
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}
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