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Some say abstraction is achieved through abstract class, while others say it is achieved through encapsulation. I am confused, would someone advise?
'Abstraction' is a very vague word covering many.many ideas in programming (and system design)
abstract classes are a very concrete mechanism in some languages.
encapsulation is a common object oriented design pattern used in some languages
Are these 2 models used for abstraction, yes. Are they the only ones, no.
I'm developing an embedded software based on standard specification.
The software is big and I need a design of my software before start coding.
the UML is an object-oriented language and could be a solution but I don't know how to use it for C softwares.
are there any document which help to use UML for C programming ?
or are there another design language for c softwares ?
Well we use Enterprise Architect to model our embedded software although we stop short of generating the code. EA can generate C code as well although we use C++ code because it feels more natural combined with UML. You can try it out and see if it fits your needs, it is not unforgivingly expensive either. It can also reverse engineer your C code so that may be a help.
Yes, you can, mostly because of two reasons:
You can actually do Object Oriented Programming with C (although it is tedious and not completely advisable), there a few books on the subject that you can read.
You can still use class diagrams to model data structures and static methods. But UML is not just about class diagrams: you also have state diagrams, interaction diagrams, use case diagrams... And they are useful for structured programming too.
I am not an expert for embedded software but have you look to https://www.polarsys.org/ ?
"PolarSys is an Eclipse Industry Working Group created by large industry players and by tools providers to collaborate on the creation and support of Open Source tools for the development of embedded systems."
It is a platform based on Eclipse dedicated to embedded software.
It use Papyrus for UML/SysML modeling and implement MARTE profil.
"Papyrus is the PolarSys solution for SysML and UML modeling. It relies on the underlying Eclipse platform and on other Polarsys and Eclipse-based offerings to fulfill lifecycle integration needs, such as a C/C++ "
I have two products in mind that could help you:
LieberLieber Software Embedded Extension, which is based on EA and gives you an idea about UML and C or
Eclipse Papyrus, which supports UML and SysML
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What is the best programming language for artificial intelligence purposes?
Mind that using suggested language I must be able to employ any AI technique (or at least most of them).
All the cool bearded gurus in what's left of AI research use Lisp :)
There are two big camps: Common Lisp and Scheme. They have different syntax, etc. Lots of good stuff written for both.
Java is a very popular all-purpose language but a lot of the interesting stuff in AI / Functional Programming, such as passing closures as first-order objects, is clumsy to do in Java.
My personal preference would be to stay away from Windowsy languages like C# and F#. Cool people develop under Unix. Or Linux if they're cool but poor.
Some cool but weird people program in Haskell. A reasonably modern FP language with good performance. I tried it once, it made my brain hurt; but you might be smarter than I am.
UPDATE: Answers to Steve's questions.
I wouldn't be the one paying for a Unix variant; that's what corporations and research institutes do. The idea is, you want to be doing AI research for an outfit that sinks millions into their hardware and doesn't balk at paying a few thousand for an operating system. That's the kind of outfit likely to have good food in the cafeteria and/or pay well for doing fun work. But I'm certainly not knocking Linux.
F# may be cool but I see a whole raft of issues getting it to run on Linux or any other Unix (that's what I meant by "windowsy"), and I don't want to work under Windows (that's what I meant by "personal preference").
To elaborate on the "windowsy" theme: You mention that F# is an OCaml variant. From my own admittedly brief research, it seems that F# is missing functors, OCaml-style objects, polymorphic variants and the camlp4 preprocessor. A functional language without functors? Really? If one were disposed to not like Microsoft, as I admittedly am, one could conclude that they had gone ahead and crowbarred a perfectly good functional language, OCaml, into something they could get to run in their CLR so they could claim to "have" a functional language. Finally, because I don't suspect, I know that Microsoft always prioritizes market dominance over product quality, I don't plan to touch F#. But this is my personal preference, and clearly identified as such, while we're really more concerned with making a good recommendation for mary.ja45 .
I have better reasons to recommend Lisp over F# and even OCaml and Haskell. These are mostly based on the historic preponderance of Lisp over any other language in the AI field.
The bulk of AI literature is based on programs written in Lisp or Prolog. If nothing else, good knowledge of Lisp would allow a student to understand the sample programs. My personal favorite AI megaproject, Cyc, has runtimes in your choice of Common Lisp or C.
In the TIOBE index of programming language (as seen and used in industry), Lisp takes 15th place while Haskell takes 43rd and F# and OCaml place below 50th. Presence on the market correlates with employment opportunities, naturally.
That said, it's quite possible that a number of the younger "AI interesting" languages are poised to skyrocket. If some major research institute published some groundbreaking, defining-the-field research in, say, Scala, you'd see Scala's popularity advance sharply in the research community and, with some lag, in industry.
I (obviously) can't comment on F#'s other qualities but you're as welcome to make recommendations as I was.
Python seems to be used a lot in the general scientific community. It has a lot of libraries available and it's easy to learn.
I'll throw Scala into the pot.
it's usable for functional programming
it can be made as fast as Java
it's a modern language with lot's of nice aspects
Java seems to be a bit popular in AI, too and so you can use all those Java libraries from Scala
I've solved all exercises from a basic AI course in Scala. It worked really well.
If by "all of AI" you also mean machine learning, which I guess, Matlab, R and Python+Scipy should definately be mentioned.
I personally use Clojure for AI programming, and have found it to be a great all-rounder AI language.
Reasons:
It's a Lisp, and Lisps have historically been very strong the the AI field
It's a homoiconic language with powerful macros, so great for code generation and genetic programming. This is a surprisingly useful property for AI programming (and possibly explains some of the success of Lisp in general in this space)
It runs on the JVM and can easily access all the Java libraries for number crunching (Weka, Colt, etc.).
It's good for rapid interactive development - it's very dynamic and you can do pretty much everything interactively in a running Clojure REPL. No need for recompiling etc.
It matters probably whether the programming environment is academic or not, but for most non-academic AI application development I would recommend sticking with a mainstream language like Java or C++. One needs to be able to interface readily with other COTS or open-source software packages, and this can sometimes be difficult or impossible in more "exotic" languages. For academic work this may be a less critical issue.
Additionally, performance can be important for many applications, and mainstream languages generally have the most heavily-optimized compilers, e.g., C++ or Java.
It is true that functional programming languages like LISP, Scheme, etc have specialized features that may make it easier to implement particular AI methods, but I do not believe this to be true for AI-related programming as a whole, e.g., quantitative machine learning methods usually don't require a functional language. If you need access to both functional constructs and general software packages, there are some tools for LISP to help with this, and the recently-developed Clojure is a LISP-variant that runs on the JVM and can access Java libraries. Also, Groovy is another JVM-based language that includes support for closures.
Lastly, some programmers like paradigm flexibility and/or fast prototyping for AI projects. Ruby and Python both see some AI-related usage for this reason as multi-paradigm languages that can also be used for scripting.
Like most things in programming, the best answer for which language to use in AI development will ultimately depend on the needs of your projects.
It really depends on what kind of problem you are looking at. Also, how "deep" you want to go into AI stuff. If you want to learn from the basics and just implement theoretical AI stuff, go with a higher level language-- as in functional programming (and proven in AI) like lisp, or prolog. If you know what problem set you are dealing with and want efficient, go with something like Java, C++ and use a toolkit to do the stuff.
Since you mention Machine Learning look into Weka Toolkit in Java for some of these stuff.
Pick the programming language with AI techniques the same way you pick a language for any other project:
What is the problem you are trying to
solve?
Is there good support available for
the language?
What are the customers requirements?
I would recommend Prolog as a very good programming language used to implement AI systems.
There is no "best" language. Each one has its merits. When I studied AI, mostly we worked with lisp and prolog, but I've been most productive in AI with Java/C# and F# has a lot to offer.
How about a framework written in Java, supporting "High Level Logic" and agent style communication.
http://highlevellogic.blogspot.com/2010/11/when-will-we-have-artificial.html
It also depends on the size of your dataset. For web-scale datasets you may want to use Map-Reduce and that implies Hadoop. Hadoop is in Java -- but you could use any language (Python, etc.) for your Map-Reduce functions.
There is also a java framework called weka, developped by the university of waikato. I don't know wether it anwsers your question, but it may help.
Quoting wikipedia: «Weka supports several standard data mining tasks, more specifically, data preprocessing, clustering, classification, regression, visualization» and more.
I am in the beginning stages of creating software for a mISV-to-be. The program is a desktop application and in the long run I want to have a native version for both Windows and OS X (I have a looked at various cross-platform APIs, and none of them meet my needs). Initially though, I don't think it makes sense to develop for two platforms at once. With that in mind, I have been looking at WPF for Windows and Cocoa for OS X, and they seem similar.
Has anyone had experience porting one to the other? Are there particular techniques/paradigms to follow that will make porting easier? Ignoring business considerations, would you recommend developing on one of them first?
Well. Once you've written an app for Cocoa, it is possible to port it to Windows. This could be done using gnustep or Cocotron.
If you do it the other way, WINE is meant to make porting easier.
I would rather write the OSX version first. This is because Windows users have no clear idea what they want an application to look like. In my experience, they are quite able to suffer through all kinds of user interfaces. Consistency has little value to them. Since there is no common agreement, what a Windows app should look like, nothing stops Windows users from actually liking OSX designs, and they even frequently do. iTunes for windows looks like a very typical OSX app and you hear very few complaints that it would not be enough Windows-ish.
Going the other direction, this is not true. OSX users have a clear preference for Cocoa apps and very little tolerance for, as an example, things like GIMP or Inkscape which work under OSX just as well as anywhere else, but look plainly ugly to the OSX trained eye.
I think that you're on the right path by choosing windowing environments that are specific to each platform. This approach allows you to create a user experience on each platform that isn't restricted by the compromises inherent to cross-platform windowing toolkits.
A good first step is to break your design down into two parts: platform specific and platform neutral elements. You can already put any UI code into the platform specific column, but maybe your app will need some data persistence that can be written in platform-neutral C++. What you may find with this approach is that there is quite a bit of logic and infrastructure that you can write in a platform-neutral way, leaving just the UI and glue code as platform-specific.
There was a recent episode of Late Night Cocoa titled Porting Large Applications to the Mac platform. Your app may not qualify as "large" but this podcast gives quite a bit of great porting knowledge from someone that's done it a few times.
I'm currently working on porting tools in this space and have many years of oft-painful experience in either using or writing cross-platform frameworks on Mac and Windows.
One of the biggest problems in the past has been Apple's refusal to open up the nib format for Cocoa (Carbon nibs were open XML files years ago). That changed with XCode 3 and the .xib format, as well explained by Frasier Speirs.
At the basic layout level, at least, there is now an opportunity to automate porting from one XML format to another. I regard WPF (XAML) as cleaner and so I'm using that as my base format and migrating to Cocoa.
When it comes to the code behind, whilst you can use C# under Mono, the CocoaSharp project seems either stalled or very slow and I wouldn't recommend it.
If you are comfortable with C++, consider having as much logic as possible in C++ with a thin platform-specific layer in C# and Objective-C.
Another approach worth investigating is using a dynamic language like Python or Ruby. I'm not sure which is more mature at present between IronPython and IronRuby but both are now supported by Microsoft people. On the Cocoa side, I think the flexibility of Ruby syntax will triumph and RubyCocoa is probably overtaking PyObjC.
Otherwise, work in C# and Objective-C and maintain two completely independent code bases with identical designs. Fortunately the frameworks have comparable semantics for most things, especially if you make use of bindings.
Well, there is not a straightforward path. The best method is to use something like Model-View-Controller pattern or some other architecture to separate business logic and so forth from the presentation. However, unless you are using Mono, there will be very little code for you to share, I think.If you are developing WPF then you surely doing .NET and, other than Mono, Objective-C is the standard programming tool under Mac OS X.
Keep a good design and you can have most of your code simply be an Objective-C version of your .NET code and vice versa rather than trying to find a migration path.
I was recently diagnosed with a cascading dissociative disorder that causes retrograde amnesia in addition to an existing case of possible anterograde amnesia. Many people have tried to remind me of how great a programmer I was before -- Right now I get the concepts and the idioms, but I want to teach myself whether I know or not. I think I can overcome the amnesia problems in part with it.
My question for you, stackoverflow, is this: I recently found Clojure and it... it feels good to use, even in just copying down the examples from whatever webpage I can find. My goals in learning a functional programming language are to create a simple webserver, an irc AI bot of some variety, and a couchdb-like database system, all of which lightweight and specifically for education. What flaws does Clojure have? Is there a better functional programming language to use right now for education /and/ application?
I think Clojure is a very nice language. If I should point to any defect it is that it's very new, and even though the language seems very mature and production ready, the tools and frameworks around it aren't. So if you are going to make, for instance, a web-app, don't expect to fire three commands and have a "Your first web app is running, now read this documentation to create your models"-page on your browser.
There aren't that many libraries written in Clojure yet either, but that's not a huge problem if you consider that you can use almost anything written in Java.
Haskell currently has a large following and a growing base of libraries and applications. It's also used for education and research. I find it a very nice language to use.
Haskell, Erlang and Clojure are all good choices. I would personally recommend Clojure, you might be able to do some interesting database stuff with the Software Transational Memory system that is part of Clojure.
You list CouchDB in your question, and it's written in Erlang, which is meant to be a pretty engrossing language once you get into it.
I have no personal experience with Clojure, but i really recommend F#. It's quite a powerful language in the style of OCaml. I really like it because it's debugging tools and IDE are second to none, and you can take advantage of practically every library on the (huge) .NET platform.