I searched about the JAGS's manual and one post (here in 2012) about the ordinary differential equation (ode). I was thinking JAGS can because it's similar to WinBUGS (which does it through WBdiff interface). However, if I let JAGS read in my ode code, it cannot even recognize the D(y[...], t) expression.
Can JAGS deal with ode? Maybe I missed a plug-in in JAGS like WBdiff?
While WinBUGS/OpenBUGS/JAGS have almost equivalent syntax/feature sets, there are a few differences between them: one of these is that there is no ODE solver included as part of a standard JAGS installation.
However, JAGS is extensible using user-specified modules (like the plug-ins you mentioned), which provide new functions/distributions using C++ code that can then be used within JAGS when that module is loaded. It would certainly be possible to implement an ODE this way using e.g. the ODE solvers included in the boost C++ library. To do so you will need the following:
Familiarity with C++
Instructions for how to build a module for JAGS
I can't help you with the former, so this may be a dead-end for you if you have never used C++ before. But there is a tutorial available for how to build a JAGS module: https://pubmed.ncbi.nlm.nih.gov/23959766/ This article shows how to build a standalone module, but if you are happy to accept the limitation of using JAGS from R (as most people do) then it is MUCH easier to build a JAGS module within an R package - you could follow code in the runjags package as an example https://cran.r-project.org/package=runjags
If you are thinking of trying to do this yourself then I could potentially help with a few pointers along the way. Of course, it is also possible that someone else has already done this, but if so then I am not aware of it.
Related
Is there a way I can use the deap library inside grasshopper's Python node
I want to run a genetic algorithm but the fitness function is to be calculated by grasshopper (only the fitness function, all the other things are to be taken of by deap inside the python node)
can it be done?
I am having problem with
importing the deap library in grasshopper's Python interface(I think I will be able to solve it by copying the files manually from Python path)
(major problem) grashopper doesn't allow closed loops so I cant seem to find a way to feed the fitness back into the Python node with the main code
couldnt get it to work, had to make do with the grasshopper pluggins
the problem was that you can only install iron python libraries for grasshopper
These are two well known issues with 'out-of-the-box' grasshopper but there are several plugins that can help overcome them.
Question One
The basic GHPython component uses Iron Python and can limit which libraries are compatible and able to be used. To get around this constraint there is a plugin called 'GH_CPython'. It allows you to set a locally installed python interpreter for your code, and then have access to any libraries available to that local interpreter. So if you install deap Libary locally then it will be available within the grasshopper GH_Cpython editor. Here is a link to download and install GH_CPython: https://www.food4rhino.com/en/app/ghcpython
Question Two
As you noted, Grasshopper is procedural and has limited support for recursive routines. To get around this there are several plugins that support recursion and may be able to help with your implementation. Which plugin would be best for your situation is difficult to say without a deeper description of your goals. Here are several options, each option provides recursive functionality that would allow for 'closed loops' where results of a script can be fed back as input.
Hoopsnake - very basic and has been around the longest
Anemone - A little more flexible and uses multiple components for loop start and end for cleaner-looking scripts. It also has a 'record history' functionality.
Octopus - Has a 'Loop' component that is similar to Hoopsnake. It also has a 'record history' functionality.
I have a Gurobi licence and I am after a good MILP/LP modelling language, which should be
free/open source
intuitive, i.e. something that looks like (taken from MiniZinc)
var int: x;
constraint x >= 0.5;
solve minimize x;
fast: the time to build the model and send it to Gurobi should be of similar order to the best ones (AMPL GAMS etc.)
flexible/powerful (ability to deal with 3D+ arrays, activate/deactivate constraints easily, provide initial solutions to the solver, etc.)
Of course, and correct me if I'm wrong, AMPL GAMS fail at 1), Python and R fail at 2) (and perhaps at 3)?).
How about GLPK, Minizinc, ZIMPL etc.? They satisfy 1) and 2) but what about 3) and 4)? Are they as good as AMPL in this regard? If not, is there a modelling language satisfying 1-4?
I've used AMPL with Gurobi for mid-sized MIPs (~ 100k-1m variables?) and MiniZinc, mostly with Gecode, for smaller combinatorial problems. I've seen some Gurobi work done with R and Python, but haven't used it that way myself.
I'm less familiar with the other options. My understanding is that GAMS is quite similar to AMPL and much of what I have to say about AMPL may also be valid for GAMS, but I can't vouch for it.
Of course, and correct me if I'm wrong, AMPL GAMS fail at 1),
Yes, generally. There is an exception which probably isn't helpful for your specific requirements but might be useful to others: you can get free use of AMPL, Gurobi, and many other optimisation products, by using the NEOS web service. This is restricted to academic non-commercial purposes and you have to grant NEOS certain rights in relation to the problems you send them; definitely read those terms of service before using it. It also requires waiting for an available server, so if speed is a high priority this probably isn't the solution for you.
Python and R fail at 2) (and perhaps at 3)?).
In my limited experience, yes for (2). AMPL, GAMS, and MiniZinc are designed specifically for defining optimisation problems, so it's unsurprising that their syntax is more user-friendly for that purpose than languages like Python and R.
The flip-side to this is that if you want to do just about anything other than defining an optimisation problem with these languages, Python/R/etc. will probably be better for that purpose.
On speed: for the problems I usually work with, AMPL takes maybe a couple of seconds to build and presolve a MIP model which takes Gurobi a couple of minutes to solve. Obviously this is going to vary somewhat with hardware and details of the problem, but in general I would expect build time to be small compared to solve time for any of the solutions under discussion. Even with a good solver like Gurobi, big MIPs are hard. Many of the serious optimisation programmers I've met do use Python, so I presume the performance side is good enough.
However, that doesn't mean the choice of language/platform is irrelevant to speed. One of the nice features of AMPL (and also GAMS) is presolve, which attempts to reduce the problem size before sending it to the solver. My standard problems have a lot of redundant variables and constraints; AMPL identifies and eliminates many of these, reducing the problem size by about 80% and giving a noticeable improvement in solver time (as compared to runs where I switch off presolve, which I sometimes do for debugging-related reasons). This might be a consideration if you expect a lot of redundancy.
flexible/powerful (ability to deal with 3D+ arrays, activate/deactivate constraints easily, provide initial solutions to the solver, etc.)
MiniZinc handles up to 6D arrays, which may or may not be enough depending on your applications.
It's more flexible than AMPL in some areas and less so in others. AMPL has a lot of set-based functionality that I find useful (e.g. I can define a variable whose index set is something like "pairs of non-identical cities separated by no more than 500 km") and MiniZinc doesn't have this. OTOH, MiniZinc seems to be better than AMPL for solver-hopping, e.g. if I write a MZ model with a combinatorial constraint like "alldifferent" but then try to run it on a solver that doesn't recognise such constraints, MZ will translate it into something the solver can deal with.
I haven't tried deactivating constraints in MZ other than by commenting them out, so I can't help there, and similarly on providing initial solutions.
Overall, MiniZinc is a good choice to consider. Some pluses and minuses relative to AMPL ("free" being a big plus!) but it fills a similar niche.
IMHO, there is no such system if you consider the Python interfaces/modeling environments to SCIP or Gurobi too complicated:
x = model.addVar()
y = model.addVar(vtype="INTEGER")
model.setObjective(x + y)
model.addCons(2*x - y*y >= 0)
model.optimize()
To me this looks quite natural and straight forward. The immense benefit of using an actual programming language instead of modeling language is that you can do anything in there, while there will always be boundaries in the latter.
If you are a looking for a modeling GUI, you should check out LITIC. It can be used almost entirely with drag-and-drop operations: https://litic.com/showcase.html
I've used a lot of the options mentioned, and some not yet mentioned
GAMS
GAMS' Python API
GAMS' MATLAB API
AMPL
FICO Xpress Mosel
FICO Xpress Model's Python API
IBM ILOG OPL
Gurobi's Python API
PuLP (Python)
Pyomo (Python)
Python-MIP
JuMP (Julia)
MATLAB Optimization Toolbox
Google OR-Tools
Based on your requirements, I'd suggest trying Python-MIP, PuLP or JuMP. They are free and have easy syntax with no limit on array dimensionality.
Take a look at Google or-tools. I’m not sure if getting initial solution to the solver is available in all of its interfaces, but if you use it in python, it should probably satisfy all 1-4.
Perhaps it's just better to describe my problem.
I'm developing a Haskell library. But part of the library is written in C, and another part actually in raw LLVM. To actually get GHC to spit out the code I want I have to follow this process:
Run ghc -emit-llvm on both the code that uses the Haskell module and the "Main" module.
Run clang -emit-llvm on the C file
Now I've got three .ll files from above. I add the part of the library I've handwritten in raw LLVM and llvm-link these into one .ll file.
I then run LLVM's opt on the linked file.
Lastly, I feed the LLVM bitcode fileback into GHC (which pleasantly accepts it) and produces an executable.
This process (with appropriate optimisation settings of course) seems to be the only way I can inline code from C, removing the function call overhead. Since many of these C functions are very small this is significant.
Anyway, I want to be able to distribute the library and for users to be able to use it as painlessly as possible, whilst still gaining the optimisations from the process above. I understand it's going to be a bit more of a pain than an ordinary library (for example, you're forced to compile via LLVM) but as painlessly as possible is what I'm looking for advice for.
Any guidance will be appreciated, I don't expect a step by step answer because I think it will be complex, but just some ideas would be helpful.
I have made some Rcpp functions for some on-going work, but the work may be performed my colleagues who are R beginners. My question is, if my colleagues want to use these function on their machines, do they have to install Rtools and use sourceRcpp() to compile the functions first? Is that any way I can avoid this? For example, use the function just like using require()?
Thanks.
On any OS, R permits you to provide a binary package. On Windows and OS X, this is even common. You did not (explicitly) state what OS your colleagues use; from context (Rtools?) it is presumably Windows. You could even use the win-builder service to compile it.
This is very basic packaging question, which is addressed in the Writing R Extensions manual. There is nothing particular about Rcpp here.
We do however have an entire vignette on packaging which you should read, and the material is also covered in the Rcpp book.
I've spent a couple of days developing a program in Haskell, while learning the language. Now I realize that I'll need to call Arpack (a Fortran library) or Arpack++ (a C++ wrapper to Arpack) -- I can't find a good implementation of Lanczos method with Haskell bindings. Do any more experienced Haskell programers have an opinion of how difficult this would be?
I've been able to get ".so" ("shared object") versions of libarpack and libarpack++ installed through Ubuntu's repository, but I'm not sure that will suffice. I suspect I'm going to ultimately need to build Arpack++ from source code, which is possible, but I'm getting a lot of build errors, so it will take time. Is there any way to use just the ".so" files, without knowing exactly which version of the header files were used to generate them?
I'm considering using GreenCard, because it looks like the most well maintained Haskell/C bridge. I can't find much documentation though, so I'm wondering whether it will support C++ too.
I'm also starting to wonder whether I should rewrite my program in Python, and use scipy to call Arpack, but I've already sunk a couple of days into writing Haskell. I really like Haskell too, so I'm hoping I can make this work. I guess my overall question is this: What would be involved in making this work with Haskell?
Thanks much.
ELF format is standard format of executables and shared libraries, so accessing the code in these compiled modules is only a matter of knowing function names. If I understand correctly, Fortran is interoperable with C. As a consequence, Fortran should be interoperable with any language which can use C bindings, including Haskell. FYI, you can find all names exported by a module (executable or shared object or simple object archive) using nm tool (it is usually available in all linux distros by default). This of course would work if the binary file was not "stripped", but AFAIK it is not common practice.
However, Haskell cannot use C++ bindings in sane way, since C++ polymorphic features require name mangling, and the method of this name transformation is highly compiler-dependent. It is well-known problem which is not specific to Haskell. Of course, you could try to get a list of exported symbols from C++ shared object and then bind them using FFI, but... It isn't worth it.
As dsign said, you can use Foreign Function Interface GHC feature to create bindings to foreign code. All you would require is library headers (and the library itself of course). In case of C language that would be header files (*.h), but since your library is written in Fortran, you have to find header files analogue in library sources, refere to this page to match Fortran and C types, and then use this information to write FFI bindings. It would be helpful first to write C bindings, i.e. write C header. Then you can even use automatic FFI binding programs like c2hs.
It maybe also helpful to look through C++ bindings. It is possible that it has the header file I've described above. If it has one, then writing FFI bindings will be no more difficult than writing them for any other library.
So, it is not entirely impossible, but it may require some thorough work. Writing bindings to scientific/pure computational libraries is way easier than writing them for some system library which does a lot of IO and keeps its own internal state, but since this library is written not in C... Well, it may be advisable to invest your time in easier alternatives. I cannot say anythin about scipy, I've never used it, but since Python as a language is much more simpler than Haskell, it may be good alternative.
I can tell you that using a C/Fortran library from Haskell, with the help of the Foreign Function Interface would be certainly possible and not terribly complicated. Here is an introduction. In my understanding, you should be able to call anything with a C calling convention, and perhaps even Fortran, without need of recompiling the code. The only exception is with things that look like function calls but are indeed macros, in which case you will have to figure out what the macros do and reproduce them in Haskell.
As of greencard, I have never used it, so I can not vouch for it.
Your second idea of using Python could potentially save you more than a couple of days. Sad as it is, I have never managed Haskell code to easily adapt to my changing requirements, while I find that trivial in Python. Of course, that could be a limitation on my skills with Haskell or my thinking process rather that something to blame to the language.