Hi,
>> I am interested in using R for machine learning (supervised
classification).
>> Currently, I have been investigating especially the rpart, tree, and
randomForest package, and have
>> achieved first results.
>>
>> are there any experiences, how the learned classificators could
>> be used in e.g. C ?
>> in other words, I want to "transfer" the learned predictor from R
>> to C-code.
>
>You could use dput to write the representation to a file, and read it
>from
>C. Parsing could be nasty, though, so I would prefer to extract the
>relevant information (e.g. fit$cptable and fit$splits in the rpart
>example),
>and write it to a database in numeric form with additional information
>if
>required. Another option could be XML (package XML), and using one of
>the
>many XML libraries in C(++).
>
thanks for your answer.
actually, taking a closer look at the representations of rpart or
randomForest, I came to a much simpler and straight-forward solution:
with only a couple of lines, the corresponding c-code for a tree can be
emitted. e.g. for randomForest:
## generate C code from rforests. generate code for tree k
gen_code=function(rfobj,k) {
tree=getTree(rfobj,k,labelVar=TRUE);
count=0;
loc.env=environment();
visit=function(idx) {
assign('count',count+1,env=loc.env);
if (tree[idx,'status']==-1)
cat(sprintf("RETURN(%s)\n",tree[idx,'prediction']))
else {
cat(sprintf("if (GETVAR(%s)<=%f) {\n",tree[idx,'split
var'],tree[idx,'split point']));
visit(tree[idx,'left daughter']);
cat("} else {\n");
visit(tree[idx,'right daughter']);
cat("}\n");
}
}
visit(1);
if (count != nrow(tree))
stop("Invalid tree in ", deparse(substitute(rfobj)))
}
however, if this is computed for 500 trees, maybe a more efficient way
to compute the result for one tree could be necessary
regards
lars
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