[Rcpp-devel] RcppArmadillo: Building with Inline

[Savitsky, Terrance]

Thanks for your reply, Dirk.  What I understand you to say is that each
time I run 
"checkLmArma(y,X)' it requires a new build of the underlying C++ code.
Actually, I should make clear the compile-link-load happens on every
invocation of checkLmArma(y,X) - not just the first.  That's fine.  I'm
not intending to use inline as a production way to build my C++ code
into an R function, but possibly as a means to test and debug.  Maybe
debugging is better done through installing a package containing the
file.  I just wanted to be sure that I wasn't experiencing some Windows
fillip that might cause the RcppArmadillo templates to run differently
than intended.  Anyway, I think I have my answer, now.

Thanks, Terrance Savitsky


-----Original Message-----
From: Dirk Eddelbuettel [mailto:edd at debian.org] 
Sent: Saturday, December 11, 2010 10:33 PM
To: Savitsky, Terrance
Cc: rcpp-devel at r-forge.wu-wien.ac.at
Subject: Re: [Rcpp-devel] RcppArmadillo: Building with Inline


On 11 December 2010 at 18:44, Savitsky, Terrance wrote:
| Hello,  I take the same fastLm code implemented in fastLmPure in
| RcppArmadillo, but built and execute through inline.  There is an
| overhead of a little over 4 seconds to execute a single iteration.  By

Just to make sure we are on the same page:  

 - you do understand that this needs to be compiled, linked and loaded
   before the first time it is run?

 - you do understand that for the same very reason none of the timings
we
   posted and and wrote about in the post you refer were done using
inline?

Dirk
   
| overhead, I mean that as I increase the row size of X, the marginal
| increase in runtime seems roughly comparable to the fastLmPure
| implementation in RcppArmadillo (as executed on my machine) so that
the
| 4 seconds seems like a fixed add.  Then I write this post to make sure
| that the overhead is driven by 'plugin = RcppArmadillo' in cxxfunction
| (and not something I'm doing incorrectly).  I'm in process of testing
my
| Windows XP hardware/software set-up before committing the work to code
| with RcppArmadillo.  The code and cxxfunction use are shown, below:
| 
| lmArma = function()
| {
| src <- '
| 	Rcpp::NumericVector yr(ys);			// creates Rcpp
| vector from SEXP
| 	Rcpp::NumericMatrix Xr(Xs);			// creates Rcpp
| matrix from SEXP
| 	int n = Xr.nrow(), k = Xr.ncol();
| 
| 	arma::mat X(Xr.begin(), n, k, false);   	// reuses memory
| and avoids extra copy
| 	arma::colvec y(yr.begin(), yr.size(), false);
| 
| 	arma::colvec coef = arma::solve(X, y);      	// fit model y ~
| X
| 	arma::colvec res = y - X*coef;			// residuals
| 
| 	double s2 = std::inner_product(res.begin(), res.end(),
| res.begin(), double())/(n - k);
| 							// std.errors of
| coefficients
| 	arma::colvec std_err = arma::sqrt(s2 * arma::diagvec(
| arma::inv(arma::trans(X)*X) ));
| 
| 	return Rcpp::List::create(Rcpp::Named("coefficients") = coef,
| 				  Rcpp::Named("stderr")       = std_err,
| 				  Rcpp::Named("df")           = n - k
| 				  );
| '
| 
| fun <- cxxfunction(signature(ys="numeric", Xs="numeric"), src,
| plugin="RcppArmadillo")
| }
| 
| checkLmArma = function(y,X)
| {
|   fun = lmArma()
|   res = fun(y,X)
|   return(res)
| }
| 
| set.seed(42)
| n <- 10000
| k <- 9
| X <- cbind( rep(1,n), matrix(rnorm(n*k), ncol=k) )
| truecoef <- 1:(k+1)
| y <- as.numeric(X %*% truecoef + rnorm(n))
| # N <- 100
| 
| tic()
| ff = checkLmArma(y,X)
| toc()
| 
| tic()
| gg = lm(y ~ X - 1)
| toc()
| 
| Thanks, Terrance Savitsky
| 
|
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-- 
Dirk Eddelbuettel | edd at debian.org | http://dirk.eddelbuettel.com