I'm cross-compling with mingw and got this error:
undefined reference to `rpl_realloc'
After some searching I found this can be resolved as follows in configure.ac or as environment variables set prior to calling ./mingw64-configure:
ac_cv_func_malloc_0_nonnull=yes
ac_cv_func_realloc_0_nonnull=yes
What defines these macros, and as there any documentation on the subject? I couldn't find any...
What defines these macros, and as there any documentation on the subject?
Autoconf uses the ac_cv_ prefix for its "cache variables", in which it records the results of configuration tests it has performed. In the event that the same check is requested multiple times, these allow it to use the previously-determined result instead of performing the check again.
The general naming convention for these is documented in the Autoconf manual. The particular cache variable names you ask about are documented to cache the results of the Autoconf's AC_FUNC_MALLOC and AC_FUNC_REALLOC macros, respectively. That documentation also speaks to the rpl_realloc name.
It is allowed to use these variables in configure.ac to programmatically determine the results of those checks, but it is a relatively nasty hack to assign values to those variables directly. In this particular case, however, the error suggests that whoever prepared the autotooling for the project you're trying to build did a sloppy job of it. If fudging the cache variables gets you a successful build and a working program then that's a tempting and much easier alternative to actually fixing the project.
This is meant to be a FAQ question, so please be as complete as possible. The answer is a community answer, so feel free to edit if you think something is missing.
This question was discussed and approved on meta.
I am using R and tried some.function but I got following error message:
Error: could not find function "some.function"
This question comes up very regularly. When you get this type of error in R, how can you solve it?
There are a few things you should check :
Did you write the name of your function correctly? Names are case sensitive.
Did you install the package that contains the function? install.packages("thePackage") (this only needs to be done once)
Did you attach that package to the workspace ?
require(thePackage) (and check its return value) or library(thePackage) (this should be done every time you start a new R session)
Are you using an older R version where this function didn't exist yet?
Are you using a different version of the specific package? This could be in either direction: functions are added and removed over time, and it's possible the code you're referencing is expecting a newer or older version of the package than what you have installed.
If you're not sure in which package that function is situated, you can do a few things.
If you're sure you installed and attached/loaded the right package, type help.search("some.function") or ??some.function to get an information box that can tell you in which package it is contained.
find and getAnywhere can also be used to locate functions.
If you have no clue about the package, you can use findFn in the sos package as explained in this answer.
RSiteSearch("some.function") or searching with rdocumentation or rseek are alternative ways to find the function.
Sometimes you need to use an older version of R, but run code created for a newer version. Newly added functions (eg hasName in R 3.4.0) won't be found then. If you use an older R version and want to use a newer function, you can use the package backports to make such functions available. You also find a list of functions that need to be backported on the git repo of backports. Keep in mind that R versions older than R3.0.0 are incompatible with packages built for R3.0.0 and later versions.
Another problem, in the presence of a NAMESPACE, is that you are trying to run an unexported function from package foo.
For example (contrived, I know, but):
> mod <- prcomp(USArrests, scale = TRUE)
> plot.prcomp(mod)
Error: could not find function "plot.prcomp"
Firstly, you shouldn't be calling S3 methods directly, but lets assume plot.prcomp was actually some useful internal function in package foo. To call such function if you know what you are doing requires the use of :::. You also need to know the namespace in which the function is found. Using getAnywhere() we find that the function is in package stats:
> getAnywhere(plot.prcomp)
A single object matching ‘plot.prcomp’ was found
It was found in the following places
registered S3 method for plot from namespace stats
namespace:stats
with value
function (x, main = deparse(substitute(x)), ...)
screeplot.default(x, main = main, ...)
<environment: namespace:stats>
So we can now call it directly using:
> stats:::plot.prcomp(mod)
I've used plot.prcomp just as an example to illustrate the purpose. In normal use you shouldn't be calling S3 methods like this. But as I said, if the function you want to call exists (it might be a hidden utility function for example), but is in a namespace, R will report that it can't find the function unless you tell it which namespace to look in.
Compare this to the following:
stats::plot.prcomp
The above fails because while stats uses plot.prcomp, it is not exported from stats as the error rightly tells us:
Error: 'plot.prcomp' is not an exported object from 'namespace:stats'
This is documented as follows:
pkg::name returns the value of the exported variable name in namespace pkg, whereas pkg:::name returns the value of the internal variable name.
I can usually resolve this problem when a computer is under my control, but it's more of a nuisance when working with a grid. When a grid is not homogenous, not all libraries may be installed, and my experience has often been that a package wasn't installed because a dependency wasn't installed. To address this, I check the following:
Is Fortran installed? (Look for 'gfortran'.) This affects several major packages in R.
Is Java installed? Are the Java class paths correct?
Check that the package was installed by the admin and available for use by the appropriate user. Sometimes users will install packages in the wrong places or run without appropriate access to the right libraries. .libPaths() is a good check.
Check ldd results for R, to be sure about shared libraries
It's good to periodically run a script that just loads every package needed and does some little test. This catches the package issue as early as possible in the workflow. This is akin to build testing or unit testing, except it's more like a smoke test to make sure that the very basic stuff works.
If packages can be stored in a network-accessible location, are they? If they cannot, is there a way to ensure consistent versions across the machines? (This may seem OT, but correct package installation includes availability of the right version.)
Is the package available for the given OS? Unfortunately, not all packages are available across platforms. This goes back to step 5. If possible, try to find a way to handle a different OS by switching to an appropriate flavor of a package or switch off the dependency in certain cases.
Having encountered this quite a bit, some of these steps become fairly routine. Although #7 might seem like a good starting point, these are listed in approximate order of the frequency that I use them.
If this occurs while you check your package (R CMD check), take a look at your NAMESPACE.
You can solve this by adding the following statement to the NAMESPACE:
exportPattern("^[^\\\\.]")
This exports everything that doesn't start with a dot ("."). This allows you to have your hidden functions, starting with a dot:
.myHiddenFunction <- function(x) cat("my hidden function")
I had the error
Error: could not find function some.function
happen when doing R CMD check of a package I was making with RStudio. I found adding
exportPattern(".")
to the NAMESPACE file did the trick. As a sidenote, I had initially configured RStudio to use ROxygen to make the documentation -- and selected the configuration where ROxygen would write my NAMESPACE file for me, which kept erasing my edits. So, in my instance I unchecked NAMESPACE from the Roxygen configuration and added exportPattern(".") to NAMESPACE to solve this error.
This error can occur even if the name of the function is valid if some mandatory arguments are missing (i.e you did not provide enough arguments).
I got this in an Rcpp context, where I wrote a C++ function with optionnal arguments, and did not provided those arguments in R. It appeared that optionnal arguments from the C++ were seen as mandatory by R. As a result, R could not find a matching function for the correct name but an incorrect number of arguments.
Rcpp Function : SEXP RcppFunction(arg1, arg2=0) {}
R Calls :
RcppFunction(0) raises the error
RcppFunction(0, 0) does not
Rdocumentation.org has a very handy search function that - among other things - lets you find functions - from all the packages on CRAN, as well as from packages from Bioconductor and GitHub.
If you are using parallelMap you'll need to export custom functions to the slave jobs, otherwise you get an error "could not find function ".
If you set a non-missing level on parallelStart the same argument should be passed to parallelExport, else you get the same error. So this should be strictly followed:
parallelStart(mode = "<your mode here>", N, level = "<task.level>")
parallelExport("<myfun>", level = "<task.level>")
You may be able to fix this error by name spacing :: the function call
comparison.cloud(colors = c("red", "green"), max.words = 100)
to
wordcloud::comparison.cloud(colors = c("red", "green"), max.words = 100)
I got the same, error, I was running version .99xxx, I checked for updates from help menu and updated My RStudio to 1.0x, then the error did not come
So simple solution, just update your R Studio
I just installed the scheme48 package from macports and have started experiencing. I was watching this youtube video, link here and was attempting to perform some of the examples. In the lecture the professor is running scheme on a Sun terminal. For example, I attempt to do '(first 473)' and get 'Error: undefined variable first'. Now, I'm assuming I haven't loaded the correct package / library or what ever it is called in scheme but am not sure what the syntax and library is. I believe that scheme48 and the scheme version on that sun terminal in the video are not the same and could be part of the problem.
So, what library do I need to use and how do I load it?
Those lecture notes are based on a book called Simply Scheme, and you can find the library code that is used in the book here. Specifically, you need simply.scm.
(But whether it is a good idea to have these kind of overloading functions is debatable. Specifically, note that first is used in a way that is different from many other languages.)
I'm using the DyninstAPI (namely, the SymtabAPI component) to rewrite the symbol tables in binaries. I'm using the following methods to do so:
data_region->setPtrToRawData((void*) new_raw, data_region->getRegionSize())
The method returns successfully, I check my error codes, and I even re-read the data section which has successfully been replaced. The problem is that the original binary isn't rewritten with the new raw .data section, and the original raw .data section persists.
I've scoured the manual to see if there is some sort of commit function but none is documented and nothing of the sort is mentioned in the examples. EDIT: I just read through some of the source code for the Region class, and it looks like I'm essentially doing what patchData does (in case that is the method I should be using).
Suggestions?
The programming manuals are available at http://www.paradyn.org/html/manuals.html.
P.S. hopefully a more reputable user can add the tags DyninstAPI and SymtabAPI for me.
After consulting with the developers, they alerted me that the function I needed to call was emit and the syntax I ended up using was:
symtab_obj->emit("new_binary.out");
Thanks Drew!
Often there's no need to pay any attention to implicit arguments in Scala, but sometimes it's very helpful to understand how the compiler is automatically providing them. Unfortunately, this understanding seems to be hard to obtain!
Is there a general method to discover how an implicit parameter has been provided, in a given piece of code?
Ideally, one day IDE integration would provide this information in some way, but I expect for now I'll have to dig deeper. Is there some way to ask the compiler to explain exactly which implicit definition it chooses at any given point? Can this be deciphered indirectly from other compiler output?
As an example, I'd like to know how to work out on my own where the implicit bf: CanBuildFrom[Repr, B, That] argument to TraversableLike.map comes from, without reading questions like this one on Stack Overflow!
Add the option -Xprint:typer to the scalac command line. This prints the program tree just after the typer compiler phase. This works best with a short, self contained example. You can also pass this to scalac. This is a really huge step towards self-reliance in Scala!
As mentioned by Randall, IntelliJ shows in-scope and the selected Implicit View with CTRL-ALT-SHIFT-I. Wait a month or two and implicit arguments are likely to have similar support.
Ideally, one day IDE integration would provide this information in some way, ...
That day is today in with JetBrains' IDEA. If you run the latest EAP of IDEA version 9 (9.0.3 EA #95.289) with a recent nightly release of the Scala plug-in, this capability is present. Every value expression may be selected and a command issued that displays a pop-up showing all applicable implicit conversions with the one the compiler will select highlighted.
And since there are apparently a few out there who don't yet know it, there is a free and open-source Community Edition of IDEA and it does support the Scala plug-in.