In short: How can I call, from within Rccp C++ code, the agrep C internal function that gets called when users use the regular agrep function from base R?
In long: I have found multiple questions here about how to invoke, from within Rcpp, a C or C++ function created for another package (e.g. using C function from other package in Rcpp
and Rcpp: Call C function from a package within Rcpp).
The thing that I am trying to achieve, however, is at the same time simpler but also way less documented: it is to directly call, from within Rcpp, a .Internal C function that comes with base R rather than another package, without interfacing with R (that is, without doing what is said in Call R functions in Rcpp). How could I do that for the .Internal C function that lays underneath base R's agrep wrapper?
The specific function I am trying to call here is the agrep internal C function. And for context, what I am ultimately trying to achieve is to speed-up a call to agrep for when millions of patterns must be each checked against each of millions of x targets.
Great question. The long and short of it is "You cant" (in many cases) unless the function is visible in one of the header files in "src/include/". At least not that easily.
Not long ago I had a similar fun challenge, where I tried to get access to the do_docall function (called by do.call), and it is not a simple task. First of all, it is not directly possible to just #include <agrep.c> (or something similar). That file simply isn't available for inclusion, as it is not a part of the "src/include". It is compiled and the uncompiled file is removed (not to mention that one should never "include" a .c file).
If one is willing to go the mile, then the next step one could look at is "copying" and "altering" the source code. Basically find the function in "src/main/agrep.c", copy it into your package and then fix any errors you find.
Problems with this approach:
As documented in R-exts the internal structures of sexprec_info is not made public (this is the base structure for all objects in R). Many internal function use the fields within this structure, so one has to "copy" the structure into your source code, to make it public to your code specifically.
If you ever #include <Rcpp.h> prior to this file, you will need to go through each and every call to internal functions and likely add either R_ or Rf_.
The function may contain calls to other "internal" functions, that further needs to be copied and altered for it to work.
You will also need to get a clear understanding of what CDR, CAR and similar does. The internal functions have a documented structure, where the first argument contains the full call passed to the function, and function like those 2 are used to access parts of the call.
I did myself a solid and rewrote do_docall changing the input format, to avoid having to consider this. But this takes time. The alternative is to create a pairlist according to the documentation, set its type as a call-sexp (the exact name is lost to me at the moment) and pass the appropriate arguments for op, args and env.
And lastly, if you go through the steps, and find that it is necessary to copy the internal structures of sexprec_info (as described later), then you will need to be very careful about when you include Rinternals and Rcpp, as any one of these causes your code to crash and burn in the most beautiful and silent way if you include your header and these in the wrong order! Note that this even goes for [[Rcpp::export]], which may indeed turn out to include them in the wrong arbitrary order!
If you are willing to go this far down the drainage, I would suggest carefully reading adv-R "R's C interface" and Chapter 2, 5 and 6 of R-ext and maybe even the R internal manual, and finally once that is done take a look at do_docall from src/main/coerce.c and compare it to the implementation in my repository cmdline.arguments/src/utils/{cmd_coerce.h, cmd_coerce.c}. In this version I have
Added all the internal structures that are not public, so that I can access their unmodified form (unmodified by the current session).
This includes the table used to store the currently used SEXP's, that was used as a lookup. This caused a problem as I can't access the modified version, so my code is slightly altered with the old code blocked by the macro #if --- defined(CMDLINE_ARGUMENTS_MAYBE_IN_THE_FUTURE). Luckily the code causing a problem had a static answer, so I could work around this (but this might not always be the case).
I added quite a few Rf_s as their macro version is not available (since I #include <Rcpp.h> at some point)
The code has been split into smaller functions to make it more readable (for my own sake).
The function has one additional argument (name), that is not used in the internal function, with some added errors (for my specific need).
This implementation will be frozen "for all time to come" as I've moved on to another branch (and this one is frozen for my own future benefit, if I ever want to walk down this path again).
I spent a few days scouring the internet for information on this and found 2 different posts, talking about how this could be achieved, and my approach basically copies this. Whether this is actually allowed in a cran package, is an whole other question (and not one that I will be testing out).
This approach goes again if you want to use not-public code from other packages. While often here it is as simple as "copy-paste" their files into your repository.
As a final side note, you mention the intend is to "speed up" your code for when you have to perform millions upon millions of calls to agrep. It seems that this is a time where one should consider performing the task in parallel. Even after going through the steps outlined above, creating N parallel sessions to take care of K evaluations each (say 100.000), would be the first step to reduce computing time. Of course each session should be given a batch and not a single call to agrep.
I am a very new Julia user (coming from Matlab), so forgive me if I ask a very dumb question.
I currently have a julia code, which works (it runs fine) though it provides different results if I execute it as a function or if I run every of the function lines interactively.
My script is mostly about linear algebra and uses Arrays and Dicts.
As I have some trouble making use of the Juno debugger, I did not find another way to debug my code, which is quite a shame.
I spent the last three hours on this and I still have no clue why these results differ.
I suspect I don't understand some very basic working process of julia related to variable allocation but I'm flying blind here.
Does anyone have a explaination for this behavior ?
I can't provide the code here but here is the base structure of the code. Basically the M matrix returned by childfunction is wrong. a is a scalar a dict is a dictionary.
calling function
function motherfunction(...)
M = childfunction(a,dict)
end
child function
function childfunction(...)
...
M = *some linear algebra*
return M
end
I was reading up on the documentation of macros and ran into the following under the `Hold up: why macros' section. The reasoning given to use macros is as follows:
Macros are necessary because they execute when code is parsed,
therefore, macros allow the programmer to generate and include
fragments of customized code before the full program is run
This leads me to wonder why someone would want to use "generate and include fragments of customized code before the full program is run". Can someone provide context as to why this would be beneficial and/or other good use cases for macros?
Let me give you my view on macros.
A macro basically is a code -> code function. It takes code (a Julia expression) as input and spits out code (a different Julia expression).
Why is this useful? It has multiple purposes:
compile time copy-and-paste: You don't have to write the same piece of code multiple times but instead can define a short macro that writes it for you wherever you put it. (example)
domain specific language (DSL): You can create special syntax that after the macros code -> code transform is replaced by pure Julia constructs. This is used in many packages to define special syntax, for example here and here.
code generation: Imagine you want to write a really long piece of code which, although being long, is very simple because it has some kind of pattern that repeats itself rather trivially. Writing that code by hand can be a pain (or even practically impossible). A macro can programmatically generate the code for you. One example is for-loop unrolling (see here and here). But even the #time macro isn't doing much more than just putting a bunch of Base.time_ns() function calls around the provided Julia expression.
special string parsing: If you type the literal 3.2 in Julia it will be parsed and interpreted as a Float64. Now, imagine you want to supply a number literally that goes beyond Float64 precision but would fit into a BigFloat. Typing big(3.123124812498124812498) won't work, because the literal number is first interpreted as a Float64 and then handed to the big function. Instead you need a way to tell Julia at parse time that this should become a BigFloat. This is handled by a #big_str 3.2 macro which (for convenience) can also be written as big"3.2". The latter is just syntactic sugar.
There might be many more applications of macros, but those are the most important to me.
Let me end by referencing Steven G. Johnson's great talk at JuliaCon 2019:
Most of the time, don't do metaprogramming :)
I am often confronted with the following situation when I debug my Julia code:
I suspect that a certain variable (often a large matrix) deep inside my code is not what I intended it to be and I want to have a closer look at it. Ideally, I want to have access to it in the REPL so I can play around with it.
What is the best practice to get access to variables several function layers deep without passing them up the chain, i.e. changing the function returns?
Example:
function multiply(u)
v = 2*u
w = subtract(v)
return w
end
function subtract(x)
i = x-5
t = 10
return i-3t
end
multiply(10)
If I run multiply() and suspect that the intermediate variable i is not what I assume it should be, how would I gain access to it in the REPL?
I know that I could just write a test function and test that i has the intended properties right inside subtract(), but sometimes it would just be quicker to use the REPL.
This is the same in any programming language. You can use debugging tools like ASTInterpreter2 (which has good Juno integration) to step through your code and have an interactive REPL in the current environment, or you can use println debugging where you run the code with #show commands in there to print out values.
Could somebody please explain to me what this message might mean?
I have the Julia client running in Atom, and my code works properly and it gets me the results, but for some line executions(ctrl+enter) the instant eval gives me "julia-client: can't render lazy".
It appears that the behind the scenes the code is executed, but the inline evaluations prefers not to output anything.
The lines corresponding to these messages usually should return a 2 dimensional arrays or dataframes, and in Julia usually the type and the dimensions are printed in the eval, but for some specific lines it can't render.
I could not find similar reports anywhere else.
julia version 0.5.0-rc3
This is a problem with package versions being out of sync. It's you're on the Julia release (v0.5), this will be fixed with a Pkg.update(). In the future, this kind of question is better suited for the Juno discussion board