Nothing
# We construct a few large matrices and we compare how much faster (slower)
# we are compared to the full matrix analysis.
# Since all the calculation are also done with full matrices, we do not
# exaggerate with the sizes.
set.seed(14)
compare <- function(expr1,expr2,tag=NULL)
{
if( !is.null(tag)) cat( "Comparing: ", tag, fill=TRUE)
print(data.frame(full=system.time( expr1, TRUE)[1:3],
sparse=system.time( expr2, TRUE)[1:3],
row.names=c("user","system","elapsed")))
}
xn <- 1000
xm <- 1200
# first start with a full matrix.
fmat1 <- matrix(rnorm(xn*xm),xn,xm)
smat1 <- as.spam(fmat1)
compare(fmat2 <- t(fmat1), smat2 <- t(smat1), "Transpose")
compare(ffmat <- fmat1 %*% fmat2,
ssmat <- smat1 %*% smat2, "multiplication")
compare( solve(ffmat), solve(ssmat), "solving")
compare(rbind(fmat1,fmat1),rbind(smat1,smat1))
compare(cbind(fmat1,fmat1),cbind(smat1,smat1))
# now create a sparse matrix.
fmat1[fmat1<3] <- 0
smat1 <- as.spam(fmat1)
compare(fmat2 <- t(fmat1), smat2 <- t(smat1), "Transpose")
compare(ffmat <- fmat1 %*% fmat2,
ssmat <- smat1 %*% smat2, "multiplication")
compare(ffmat <- ffmat + diag(xn),
ssmat <- ssmat + diag.spam(xn), "add identity")
compare(ffmat <- 1:xn %d+% ffmat,
ssmat <- 1:xn %d+% ssmat, "add identity quicker")
compare( solve(ffmat), solve(ssmat), "solving")
summary(ssmat)
# compare a few cbind/rbinds
compare(rbind(fmat1,fmat1),rbind(smat1,smat1))
compare(cbind(fmat1,fmat1),cbind(smat1,smat1))
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.