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Is there a ready template to run evolutionary\genetic algorithms in R?
I am interested in a code that would allow me to add graphical output and user input between iterations.
Thanks for reading!
p.s. found this posting Is there any Genetic Programming code written R
I don't know about genetic programming code written in R, but there is a program called HeuristicLab you can use.
There you can add an External Evaluator in R code and there you can add your graphical output.
Here is a link on how to do it:
http://dev.heuristiclab.com/trac/hl/core/wiki/UsersHowtosOptimizingExternalApplications
Its an open source program and the staff that wrote it usually answers any question you have very quickly.
Here is the download page: http://dev.heuristiclab.com/trac/hl/core/wiki
good luck!
Yep, i think http://rsymbolic.org/projects/show/rgp is exactly what you're looking for.
I would like to convert an ARIMA model developed in R using the forecast library to Java code. Note that I need to implement only the forecasting part. The fitting can be done in R itself. I am going to look at the predict function and translate it to Java code. I was just wondering if anyone else had been in a similar situation before and managed to successfully use a Java library for the same.
Along similar lines, and perhaps this is a more general question without a concrete answer; What is the best way to deal with situations where in model building can be done in Matlab/R but the prediction/forecasting needs to be done in Java/C++? Increasingly, I have been encountering such a situation over and over again. I guess you have to bite the bullet and write the code yourself and this is not generally as hard as writing the fitting/estimation yourself. Any advice on the topic would be helpful.
You write about 'R or Matlab' to 'C++ or Java'. This gives 2 x 2 choices which is too many degrees of freedom for my taste. So allow me to concentrate on C++ as the target.
Let's consider a simpler case: Prototyping in R, and deploying in C++. If and when the R package you use is actually implemented in C or C++, this becomes pretty easy. You "merely" need to disentangle the routine you are after from its other dependencies (header files, defines, data structures, ...) and provide it with the data and parameters needed. I have done that in the past for production systems.
Here, you talk about the forecast package. This happens to depend on the RcppArmadillo package which itself brings the nice Armadillo C++ library to R. So chances are you can in fact re-write this as a self-contained unit.
Armadillo is also interesting when you want to port Matlab to C++ as it is written to help with exactly that task in mind. I have ported some relatively extensive Matlab code to C++ and reaped a substantial speed gain.
I'm not sure whether this is possible in R, but in Matlab you can interact with your Matlab code from Java - see http://www.cs.virginia.edu/~whitehouse/matlab/JavaMatlab.html. This would enable you to leave all the forecasting code in Matlab and have e.g. an interface written in Java.
Alternatively, you might want to have predictive code written in Java so that you can produce a model and then distribute a program that uses the model without having a dependency on Matlab. The Matlab compiler maybe be useful here, but I've never used it.
A final simple way of interacting messily between Matlab and Java would be (on linux) using pseudoterminals where you would have a pty/tty pair to interface Java and Matlab. In this case you would send data from Java to Matlab, and have Matlab return the forecasting results. I expect this would also work in R, but I don't know the syntax.
In general though, reimplementing the code is a decent solution and probably quicker than learning how to interface java+matlab or create Matlab libraries.
Some further information on the answer given by Richante: Matlab has some really nice capabilities for interop with compiled languages such as C/C++, C#, and Java. In your particular case you might find the toolbox Matlab Builder JA to be particularly relevant. It allows you to export your Matlab code directly to Java, meaning you can directly call code that you've constructed during your model-building phase in Matlab from Java.
More information from the Mathworks here.
I am also concerned with converting "R to Java" so will speak to that part.
As Vincent Zooneykind said in his comment - the PMML library in R makes sense for model export in general but "forecast" is not a supported library as of yet.
An alternative is to use something like https://www.opencpu.org/ to make a call to R from your java program. It surfaces the R code on a http server. Can then just call it with parameters as with a normal http call and return what is neede using java.net.HttpUrlConnection or a choice of http libraries available in Java.
Pros: Separation of concerns, no need to re-write the R code
Cons: Invoking an R server in your live process so need to make sure that is handled robustly
I am doing matrix operations on large matrices in my C++ program. I need to verify the results that I get, I used to use WolframAlpha for the task up until now. But my inputs are very large now, and the web interface does NOT accept such large values (textfield is limited).
I am looking for a better solution to quickly cross-check/do math problems.
I know there is Matlab but I have never used it and I don't know if thats what will suffice my needs and how steep the learning curve would be?
Is this the time to make the jump? or there are other solutions?
If you don't mind using python, numpy might be an option.
Apart from the license costs, MATLAB is the state of the art numerical math tool. There is octave as free open source alternative, with a similar syntax. The learning curve is for both tools absolutely smooth!
WolframAlpha is web interface to Wolfram Mathematica. The command syntax is exactly the same. If you have access to Mathematica at your university, it would be most smooth choice for you since you already have experience with WolframAlpha.
You may also try some packages to convert Mathematica commands to MATLAB:
ToMatlab
Mathematica Symbolic Toolbox for MATLAB 2.0
Let us know in more details what is your validation process. How your data look like and what commands have you used in WolframALpha? Then we can help you with MATLAB alternative.
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I have been working on a few R packages for some general tools that aren't currently available in R: blogging, report delivery, logging, and scheduling. This led me to wonder: what are the most important things that people wish existed in R that currently aren't available?
My hope is that we can use this to pinpoint some gaps, and possibly work on them collaboratively.
I'm a former Mathematica junkie, and one thing that I really miss is the notebook style interface. When I did my research with notebooks, papers would almost write themselves as I did my analysis. But now that I'm using R, I find that documenting my work to be quite tedious.
For people that are not so familiar with Mathematica, you have documents called "notebooks" that can contain code, text, equations, and the results from executed code (which can be equations, text, graphics, or interactive tools). Everything can be neatly organized into styled subsections or sections that are collapsable. You can have multiple open documents that integrate with a single shared kernel.
While I don't think a full-blown Mathematica style interface is entirely necessary, some interactive document system that would support text (for description), code, code output, and embedded image output would be a real boon to researchers.
A Real-Time R package would be my choice, using C Streaming perhaps.
Also I'd like a more robust web development package. Nothing as extensive as Ruby on Rails but something a bit better than Sweave combined with R2HTML, that can run on RApache. I think this needs to be a huge area of emphasis for R in general.
I realize LaTeX is better markup for certain academia but in general I think HTML should be the markup language of choice. More needs to be done in terms of R Web Apps, so applications can be hosted on huge RAM remotely and R can start being used for SaaS data applications and other graphics choices.
Interfaces to any of the new-fangled 'Web 2.0' databases that use key-value pairs rather than the standard RDMS. A non-exhaustive list (in alphabetical order) would be
Cassandra Project
CouchDB
MongoDB
Project Voldemort
Redis
Tokyo Cabinet
and it would of course be nice if we had a DBI-alike abstraction on top of this. Jeff has started with RBerkeley but that use the older-school Oracle BerkeleyDB backend rather than one of those new things.
An output device which produces Javascript code, perhaps using the protovis library.
as a programmer and writer of libraries for colleagues, I was definitely missing a logging package, I googled and asked around, here too, then wrote one myself. it is on r-forge, here, and it s called "logging" :)
I use it and I'm obviously still developing it.
There are few libraries to interface with database in general, and there is not ORM library.
RMySQL is useful, but you have to write the SQL queries manually and there is not a way to generate them as in a ORM. Morevoer, it is only specific to MySQL.
Another library set that R still doesn't have, for me, it is a good system for reading command line arguments: there is R getopt but it is nothing like, for example, argparse in python.
A natural interface to the .NET framework would be awesome, though I suspect that that might be a lot of work.
EDIT:
Syntax highlighting from within RGui would also be wonderful.
ANOTHER EDIT:
R.NET now exists to integrate R with .NET.
A FRAQ package for FRequently Asked Questions, a la fortune(). R-help would be so much fun: "Try this, library(FRAQ); faq("lattice won't print"), etc.
See also.
A wiki package that adds wiki-like documentation to R packages. You'd have a inst/wiki subdirectory with plain text files in markdown, asciidoc, textile, with embedded R code. With the right incantation, these files would be executed (think brew and/or asciidoc packages), and the relevant output uploaded to a given repository online (github, googlecode, etc.). Another function could take care of synchronizing the changes made online, typically via svn or git.
Suddenly you have a wiki documentation for your package with reproducible examples (could even be hooked to R CMD check).
EDIT 2012:
... and now the knitr package would make this process even easier and neater
I would like to see a possibility to embed another programming language within R in a more straightforward way by the users. I give this as an example in some common-lisp implementations one could write a function with embedded C code like this:
(defun sample (x)
(ffi:c-inline (n1 n2) (:int :int) (values :int :int) "{
int n1 = #0, n2 = #1, out1 = 0, out2 = 1;
while (n1 <= n2) {
out1 += n1;
out2 *= n1;
n1++;
}
#(return 0)= out1;
#(return 1)= out2;
}"
:side-effects nil))
It would be good if one could write an R function with embedded C or lisp code (more interested in the latter) in a similar way.
A native .NET interface to RGUI. R(D)Com is based on COM, and it only allows to exchange matrices, not more complex structures.
I would very much like a line profiler. This exists in Matlab and Python, and is very useful for finding bits of code that take a lot of time or are executed more (or less) than expected. A lot of my code involves function optimizations and how many times something iterates may not be known in advance (though most iterations are constrained or specified).
The call stack is useful if all of your code is in R and is very simple, but as I recently posted about it, it takes a painstaking effort if your code is complex.
It's quite easy to develop a line profiler for a given bit of code. A naive way is to index every line (or just pre-specified sections) and insert a call to log proc.time() that line. In a loop, I simply enumerate sections of code and store in a 2 dimensional list the proc.time values for section i in iteration k. [See update below: this isn't actually a way to do a line profiler for all kinds of code.]
One can use such a tool to find hotspots, anomalies (e.g. code that should be O(n) but is really O(n^2)), code that may benefit from memoization (a line profiler doesn't tell you this, but it lets you know where to look), code that is mistakenly inside a loop, and more.
Update 1: Inserting a timing line between every function line is slightly erroneous: the definition of a line of code is not simply code separated by whitespace. Being able to parse the code into an AST is necessary for knowing where operations begin and end. As discussed in some of the answers to this question, there are some tools (namely, showTree and walkCode in the codetools package) for doing this. Simply applying a regular expression to source code would be a very bad thing to do.
I've been looking for some time for a word processor to use for writing technical papers and I haven't really found one. What would really be nice to have is an editor that can handle mathematical expressions, code, and pseudo-code fairly well. I have yet to find one that works very well.
Does anyone have any recommendations?
I personally believe in LaTeX.
Benefits:
You can focus on content over form.
Use logical rather than semantic formatting (e.g., \methodname vs. just italic).
Easier to assemble large documents from multiple files.
Use text-based version control (CVS/SVN/etc.)
Widely used
Much more stable even on super-weak machines
Programmable. For example, I use macros to hide stuff, highlight stuff, obfuscate names by using a macro name with the real name but an obfuscated replacements.
See all the tips and tricks available on SO.
Output looks the same no matter which platform you compile on. Never had that luck with word, each version and each machine produces something slightly different.
My answer's long, so I want to say up front: I think you want OpenOffice Writer (I use v2.4, haven't tried 3.0 yet).
I've used Word with equation editor and LaTeX heavily in the past and OpenOffice Writer
more recently. I used the former two while writing my thesis.
LaTeX may still have advantages in quality of the output and in the ability to use text-based version control, but they're sharply diminished by OO Writer at this point.
Microsoft with equation editor, even the most recent versions, seems very weak still.
What I like about OpenOffice is that you can use the equation formatting mechanisms
in a mode where the window is split between the document you're writing and another
area where you can type very LaTeX-like formatting instructions. One of the big
strengths of LaTeX is that you get to type up something like $x \in S$ for "x is an element of S". OO Writer lets you do this and see the result.
Back when I wrote my thesis, LaTeX was preferable to Word with Eqn. Editor because of the length of my document (over 200 pages), the quality of the results, and the ease of specifying equations. LaTeX does have a disadvantage in simplicity of use that is made more acute by OO Writer.
That said, I'm sure I'd use OO Writer for conference to journal length articles (~8-15 pages v. ~15-40 pages) and also for shorter work. For thesis-length work, I'm not sure which I'd end up using: Word never worked so well for me on longer matter; I suspect OO Writer is better behaved but I don't have enough experience of it to make a firm judgement.
I like LyX (http://www.lyx.org/) -- it's a good tradeoff between "spending all your time writing your document" and "spending all your time writing markup". The most recent versions are even useable!
Apart from that, Word 2008 is actually pretty darn good, provided you use the styles and other "advanced" features.
I fully agree that LATEX is a good choice. I've used for paper in univ, including my master thesis. For LATEX I've been using Kile.
But nowadays there is interesting alternative which is DocBook with MathML extension.
LaTeX with TexMaker got me through grad school.
Depends on what you mean by "Word Processor". If you don't mind not having a WYSIWYG interface, I'd recommend LaTeX (http://www.latex-project.org/).
I wrote my final year Master's dissertation using it, which contained a lot of pseudocode, formulas, etc. Also outputs in a format fairly typical of technical papers.
I use FrameMaker.
MS Word with Mathtype. It has a number of advantages over the default Equation editor, including, but not limited to:
keyboard shortcuts
writing equations in tex mode then converting them
converting equations from "normal" to "linear" mode (the one you can use in your programs, you know a=b/c and such)
templates
no more latex. I can concentrate on the material, not the writing
Word with MS Equation for the mathematical sections.
I like DocBook and use FOP to create PDFs from it.
I use reStructuredText because it can be used in Trac, converted to PDF and HTML, have little markup overhead, and looks nice in its plain form too.
Microsoft Word is considered as the market standard word editor.
My suggestion is for you to use Authorea.
As a former postdoc (Astrophysics) and Ph.D. (Informatics) with 12+ years research experience (Harvard, CERN, UCLA), I have written technical papers for a long time. I have loved and hated LaTeX. For the past 2 years, I have worked with friends and colleagues at developing the next generation platform for writing technical/research documents collaboratively. It is called Authorea. From a technical standpoint Authorea is built on Git and takes LaTeX, Markdown, HTML (even JS, to include fancy d3.js in your papers). Bonus: you don't need to know LaTeX (or any other format) but you can easily add equations, tables, citations, and data to your papers. I hope you'll find it useful.