Nothing
print.dsm <- function (x, ...) {
info <- check.dsm(x)
cat(sprintf("Distributional Semantic Model with %d rows x %d columns\n", info$nrow, info$ncol))
n.cells <- prod(info$nrow, info$ncol)
qformat <- function (y) {
if (is.na(y)) return("N/A")
if (y > 9.9999e9) return(sprintf("%.1fG", y/1e9))
if (y > 9.9999e6) return(sprintf("%.1fM", y/1e6))
if (y > 50e3) return(sprintf("%.1fk", y/1e3))
return(sprintf("%d", y))
}
if (info$M$ok) {
cat("* raw co-occurrence matrix M available\n")
if (info$M$sparse) {
n.nz <- signcount(x$M, "nnzero")
cat(sprintf(" - sparse matrix with %s / %s nonzero entries (fill rate = %.2f%%)\n", qformat(n.nz), qformat(n.cells), 100 * n.nz / n.cells))
} else {
cat(sprintf(" - dense matrix with %s cells\n", qformat(n.cells)))
}
if (info$M$canonical) cat(" - in canonical format\n")
if (isTRUE(info$M$nonneg)) cat(" - known to be non-negative\n")
if (!is.na(info$N)) cat(sprintf(" - sample size of underlying corpus: %s tokens\n", qformat(info$N)))
}
if (info$S$ok) {
cat("* scored matrix S available\n")
if (info$S$sparse) {
n.nz <- signcount(x$S, "nnzero")
cat(sprintf(" - sparse matrix with %s / %s nonzero entries (fill rate = %.2f%%)\n", qformat(n.nz), qformat(n.cells), 100 * n.nz / n.cells))
} else {
cat(sprintf(" - dense matrix with %s cells\n", qformat(n.cells)))
}
if (info$S$canonical) cat(" - in canonical format\n")
if (isTRUE(info$S$nonneg)) cat(" - known to be non-negative\n")
}
invisible(x)
}
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