I am enjoying the puppet declarative model. I'd like to try to incorporate more of this into my code.
I program in python currently, however, and I tend to think imperatively while doing so. It's just like my problem with graphic design: I know what I like to see in an end product, but have no idea how to assemble it.
How can I structure things so that the code would be declarative? What initial steps are taken if the solution is to be recognizably "declarative?"
Functional, Declarative, and Imperative Programming I've just read this, bringing insight, a bit.
Not enough though, can't put it into words exactly, my confusion :(
EDIT: Words have come: The examples given for declaration always are given in terms of some other high level thing:
Regexen are declarative, sure, but you make the engine out of C.
Make is declarative,certainly but it's written in C.
Puppet mainifests are declarative of course, but the Ruby code is not.
So at what point do I say: "Ok, here is the methods, now I can begin the declarative part" ?
When in a functional language, write in a functional style, when in in an imperative language write in an imperative style, and when in a declarative language, write in a declarative style.
As per above (excluding all the "cross styles" allowed), when you write in a language you write in the style of a language. In the case of writing 'declartively' in an 'imperative' or 'functional' language this would usually break down to writing a declarative DSL and/or API without actually changing the fundamental style used to expose (or even glue) said declarative-friendly DSL/API.
For instance, make (or any number of XML-languages like ant) are just declarative DSLs (with some blurring). The host language is not important. This can be generalized for pretty much all Turing Complete host languages.
Related
In this blog entry by Jim Duey - he provides a list of reasons that you'd want to use monads. One his reasons is this:
So what are some clues that a monadic solution is possible? It seems to me that anytime you're copying and pasting code to define a new function that's similar to an existing one, there might be a monad lurking.
This is actually quite similar to the justification for using a Clojure style macro.
In this presentation by Aaron Bedra - he talks about a use case for Macros when writing a redis driver to generate json. On slide 66 - he shows an example of this.
(defmacro defcommand
[name params]
(let [p (parameters params)]
`(defn ~name ~params
My question is - can Monads be used to solve this problem of duplicate code when generating json for redis calls instead of a macro?
Assumptions
I understand it is more idiomatic to choose a macro over a monad in Clojure. For the purpose of this question I'm choosing to ignore what is idiomatic and just look at what is possible.
anytime you're copying and pasting code to define a new function
that's similar to an existing one
I would create a higher order function to get rid of copy paste, as simple as that.
Now, Macro or Monad or even a simple higher order function solves a single purpose but at different conceptual levels. The purpose is to "abstract away a common pattern in a system".
The different conceptual levels at which you can see a pattern appearing in a system are:
Same chunk of code - Use function / higher order function
Same chunk of code but that require some sort of "compile time" preprocessing to make it abstract - Macro
Pattern of "A value inside a box with some context" and it fits with the monad laws - Monads. (Maybe, IO, List - all these are API patterns where we see a value inside a box with some context)
I know it sounds very "abstract", but once you practice enough thinking about how to abstract general patterns in your system, you will eventually get the intuition about which tool to use for which pattern.
Monads and Macros operate at different levels of abstraction.
I think of macros as "code monkeys": anytime I find myself writing a lot of boilerplate, I take a moment and think whether macros can help.
Monads on the other hand are very powerful abstractions generally used in order to isolate and compose side effects in a purely functional setting. (I'm not an expert on Monads so take this with a grain of salt).
The bottom line is that I think they are solving fundamentally different problems.
If we take the example you provided, writing a Redis driver, any function accessing the network is a candidate for an abstraction through monads - in this case, both the IO and Maybe (or Either) monads. But not because of repetition, but rather because your functions have side effects.
Now this is where Monads in clojure can get harder than it's worth: If you want to compose both monads, you need to use monad transformers. Obviously this is not enforced by clojure since it's a dynamic language so one can argue debugging composed monadic code in a dynamic language can be very hard. A static type system such as Haskell's would be of great help here.
So while a redis driver can definitely be written in a monadic style, I believe the point Aaron is making in the presentation is avoiding repetition, and for that, I believe macros are a better fit in Clojure.
I hope this is helpful.
EDIT:
I should also node that monads in Clojure would be hardly useful without the Haskell inspired do notation which is essentially made possible by macros, providing further proof that macros are at a different level of abstraction.
I have been delivering training on Programming Practices and on Writing Quality Code to participants who have been working on Java since sometime. Object Oriented Analysis and Design is the base and I cover S.O.L.I.D. Principles and excerpts from books like Clean Code, Code Complete 2 and so on.
I am scheduled to deliver training to Perl Programmers(with less than 1 yr. exp. in Perl) in two days and they do not use the Moose(an extension of the Perl 5 object system which brings modern object-oriented language features).
I am now confused as to how to structure my training as they don't follow OOPs.
Any suggestions?
Regards,
Shardul.
Even without Moose, object-oriented programming in Perl is quite possible, and very common. Many CPAN modules offer their functionality through an object-oriented API, even if many of these also offer a non–object-oriented API. (A good example of this duality is IO::Compress::Zip.) Obviously the norms of object-oriented design in Perl are somewhat different from those in some languages — encapsulation is not enforced by the language, for example — but the overall principles and practices are the same.
And even without any sort of object-oriented programming, Moosish or otherwise, there's plenty to talk about in terms of laying out packages, organizing code into functions/subroutines/modules, structuring data, taking advantage of use warnings (or -w) and use strict and -T and CPAN modules, and so on.
I'd also recommend Mark Jason Dominus's book Higher-Order Perl, which he has made available for free download. I don't know to what extent you can race through the whole book in a day and put together something useful in time for your presentation — functional programming is a bit of a paradigm-shift for someone who's not used to it (be it you, or the programmers you're presenting to!) — but you may find some useful things in there that you can use.
A lot of the answers here are answers about teaching OOP to Perl programmers who don't use it, but your question sounds like you're stymied on how to teach a course on code quality, in light of the fact that your Perl programmers do not use OOP, not specifically that you want to teach OOP to non-OO programmers and force them into that paradigm.
That leaves us with two other paradigms of programming which Perl supports well enough:
Good ol' fashioned Structured Programming also Modular Programming
Functional programming support in Perl (also Higher-Order Perl)
I use both of these--combined with a healthy dose of objects, as well. So, I use objects for the same reason that I use good structure and modules and functional pipelines. Using the tool that brings order and sanity to the programming process. For example, object-oriented programming is the main form of polymorphism--but OOP is not polymorphism itself. Thus if you are writing idioms that assist in polymorphism, they assist in polymorphism, they don't have to be stuck in some ad-hoc library "class" and called like UtilClass->meta_operator( $object ) which has little polymorphism itself.
Moose is a great object language, but you don't call Moose->has( attribute => is => 'rw', isa => 'object' ). You call the operator has. The power of Moose lies in a library of objects that encapsulate the meta-operations on classes--but also in simple expressive operators that the rather open syntax of Perl allows. I would call that the appreciation of solving the problems that OOP solves with objects.
Also, I guess I have a problem with your problem, because "not OOP" is a big field. It can range from everything-in-the-mainline coding to not-strictly-OOP (where the process of programming is not simply OOP analysis). So I think you have to know your audience and know what it is they use to keep that code structured and sane. I can't imagine a modern Perl audience that isn't at least object-users.
From there, Perl Best Practices (often abbreviated PBP) can help you. But so would learning that
simply because OOP is one of the best supports for polymorphism it isn't polymorphism in itself
simply because OOP is one of the best supports for encapsulation it isn't encapsulation in itself.
That OOP has been assisted by structured and modular programming--and is not by itself those things. Some of its power is simply just those disciplines.
In addition, as big as an object author and consumer I am, OOP is not the way I think. Reusability is the way I think: What have I done before that I do not want to write again? What have I written that is similar? How can I make my current task just an adapter of what has been written before. (And often: how can I sneak my behavior branch an established module in a single line?)
As a result, a number of my constructs would fail the pedestrian goal of OOP. To give you a better view: I divide code into two "domains": Highly abstract and polymorphic Library code, and the Scripting that I need to do to get the particular function that I'm required in a current project. (this is essentially what "application" means, but I don't think it would be as clear). As a result, polymorphism is mainly instrumental in providing adaptability, but the adaptation itself is whatever takes the least lines of code. My optimum system would be a library that allows scripting/adaptation at any juncture between library behavior and a set of configurations or scripts that address a particular problem. Again, if I had my druthers, configuration would be injected from the scripted domain and no library code would say "I need a properties file" by itself, unless it was a library module encapsulating the algorithm of configuration instanced in properties files. It would just know that it needs "policies" (or decisions from the application domain) in order to fulfil its function.
Thus, my ideal application contains special purpose "objects", which conform to "roles" but where classes are useless overhead--except that the classes perform the behavior which allow injectable data and behavior. So some of my Perl "objects" violate OOP analysis, because they are simply encapsulations of one-off solutions, kind of like the push-pin (expando) JavaScript objects.
I will often (later) revise a special-purpose object and push it further back into the library domain as I find that I need to write something like this again. All objects in the library domain are just on some level of the spectrum of specified behavior. Also, I arrange "data networks" where there is a Sourced type of class that simply encapsulates the behavior of accessing data either in the object itself or another source object. This helps speed my solutions immensely, but I've never seen it addressed in any duck-cat-dog-car-truck OOP primer. Also templating--especially when combined with "data networks"--immensely useful in coding solutions in a half-dozen lines or a half-day of work.
So I guess I'm saying, to the extent that you only know OOP for structuring programming, you won't be able to appreciate how much some older, sound practices or other paradigms do for you--or how things that qualify as OOP can promote mediocre adaptability. (Besides components are far more current than "objects".) Encapsulation solves many problems, but it also promotes the lack of data where you need it. The idea is to get data where you need it so that your canned behavior can realize the specifics of the problem and operate on that.
Reread some stuff on structured programming
Read some stuff on functional programming (assuming that you're not already familiar with it.)
Also it's possible that even an established, "productive" Perl team is writing ... crap. If they are not OOP programmers because they are simply writing crap code, then by all means teach them OOP and if they lack even structured programming *shove both of them down their throats* (I have a hard time considering the label "professional", here).
Take a good look at 'Perl Best Practices' by Damian Conway. It has lots of solid material in it, and you won't go far wrong taking his advice.
Be aware, though, that Getopt::Clade is only available as a placeholder package - it is vapourware, in other words.
You might want to look at what's covered in the "Modern Perl" book too:
http://onyxneon.com/books/modern_perl/
As the others say - plenty to cover without Moose.
Setting up modules/distros
Testing and TAP
Deployment with cpanm / cpan / local::lib
Important changes 5.8 5.10 vs 5.12 vs 5.14, autodie etc.
Perl programmers must know about Perl's weakly functional features, like list contexts, map, grep, etc. A little functional style makes Perl infinitely more readable.
Perl programmers must also understand Perl's traditional OO features, especially modules, bless, and tie. Make them write an object or maybe tie a Cache::Memcached object around a query or something.
I am working on a Clojure project and I often find myself writing Clojure macros for DSLs, but I was watching a Clojure video of how a company uses Clojure in their real work and the speaker said that in practical use they do not use macros for their DSLs, they only use macros to add a little syntactic sugar. Does this mean I should write my DSL in using standard functions and then add a few macros at the end?
Update:
After reading the many varied (and entertaining) responses to this question I have realized that the answer is not as clear cut as I first thought, for many reasons:
There are many different types of API in an application (internal, external)
There are many types of user of the API (business user who just wants to get something done fast, Clojure expert)
Is there macro there to hide boiler plate code?
I will go away and think about the question more deeply, but thanks for your answers as they have given me lots to think about. Also I noticed that Paul Graham thinks the opposite of the Christophe video and thinks macros should be a large part of the codebase (25%):
http://www.paulgraham.com/avg.html
To some extent I believe this depends on the use / purpose of your DSL.
If you are writing a library-like DSL to be used in Clojure code and want it to be used in a functional way, then I would prefer functions over macros. Functions are "nice" for Clojure users because they can be composed dynamically into higher order functions etc. For example, you are writing a functional web framework like Ring.
If you are writing a imperative DSL that will be used pretty independently of other Clojure code and you have decided that you definitely don't need higher order functions, then the usage will be pretty similar and you can chose whichever makes most sense. For example, you might be creating some kind of business rules engine.
If you are writing a specialised DSL that needs to produce highly performant code, then you will probably want to use macros most of the time since they will be expanded at compile time for maximum efficiency. For example, you're writing some graphics code that needs to expand to exactly the right sequence of OpenGL calls......
Yes!
Write functions whenever possible. Never write a macro when a function will do. If you write to many macros you end up with somthing that is much harder to extend. Macros for example cant be applied or passed around.
Christophe Grand: (not= DSL macros)
http://clojure.blip.tv/file/4522250/
No!
Don't be afraid of using macros extensively. Always write a macro when in doubt. Functions are inferior for implementing DSLs - they're taking the burden onto the runtime, whereas macros allows to do many heavyweight computations in a compilation time. Just think of a difference of implementing, say, an embedded Prolog as an interpreter function and as a macro which compiles Prolog into some form of a WAM.
And do not listen to those who say that "macros cant be applied or passed around", this argument is entirely a strawman. Those people are advocating interpreters over compilers, which is simply ridiculous.
A couple of tips on how to implement DSLs using macros:
Do it in stages. Define a long chain of languages from your DSL to the underlying Clojure. Keep each transform as simple as possible - this way you'd be able to easily maintain and debug your DSL compiler.
Prepare a toolbox of DSL components that you will reuse when implementing your DSLs. It should include target languages of different semantics (e.g., untyped eager functional - it is Clojure itself, untyped lazy functional, first order logic, typed imperative, Hindley-Millner typed eager functional, dataflow, etc.). With macros it is trivial to combine properties of all that target semantics seamlessly.
Maintain a set of compiler-building tools. It should include parser generators (useful even if your DSLs are entirely in S-expressions), term rewriting engines, pattern matching engines, implementations for some common algorithms on graphs (e.g., graph colouring), etc.
Here's an example of a DSL in Haskell that uses functions rather than macros:
http://contracts.scheming.org/
Here is a video of Simon Peyton Jones giving a talk about this implementation:
http://ulf.wiger.net/weblog/2008/02/29/simon-peyton-jones-composing-contracts-an-adventure-in-financial-engineering/
Leverage the characteristics of Clojure and FP before going down the path of implementing your own language. I think SK-logic's tips give you a good indication of what is needed to implement a full blown language. There are times when it's worth the effort, but those are rare.
I realize that it is impossible to have one language that is best for everything.
But there is a class of simple programs, whose source code looks virtually identical in any language.
I am thinking not just "hello world", but also arithmetics, maybe string manipulation, basic stuff that you would typically see in utility classes.
I would like to keep my utilities in this meta-language and have it automatically translated to a bunch of popular languages. I do this by hand right now.
Again, I do not ask for translation of every single possible program. I am thinking a very limited, simple language, but superportable.
Do you know of anything like that? Is there a reason why it should not exist?
Check Haxe, and its Wikipedia page. It's open source and its main purpose is what you describe: generating code in many languages from only one source.
Just about any language that you choose is going to have some feature that doesn't map to another in a natural way. The closest thing I can think of is probably a useful subset of JavaScript. Of course, if you are the language author you can limit it as much as you want, providing only constructs that are common to just about any language (loops, conditionals, etc.)
For purposes of mutability, an XML representation would be best, but you wouldn't want to code in it.
If you find that there is no universal language, you can try a pragmatic model-driven development approach, using a template-based code generator.
On the template you keep the underlying concepts of an algorithm. Then, you would add code for this algorithm in one or more specific languages (C++,Java,JS,Python) when necessary. You would have to do it anyway, whatever the language or approach you choose. A configuration switch would pick the correct language for any template you apply.
AtomWeaver is a code generator that works with templates and employs ABSE as the modeling approach.
I did some looking and found this.
https://www.indiegogo.com/projects/universal-programming-language
looks interesting
A classic Pascal is very simple. Oberon is another similar option. Or you could invent your own derivative language similar to the pseudocode from the computer science textbooks. It's trivial to implement a translator from one of that languages into any decent modern imperative language.
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Why is Lisp used for AI?
What makes a language suitable for Artificial Intelligence development?
I've heard that LISP and Prolog are widely used in this field. What features make them suitable for AI?
Overall I would say the main thing I see about languages "preferred" for AI is that they have high order programming along with many tools for abstraction.
It is high order programming (aka functions as first class objects) that tends to be a defining characteristic of most AI languages http://en.wikipedia.org/wiki/Higher-order_programming that I can see. That article is a stub and it leaves out Prolog http://en.wikipedia.org/wiki/Prolog which allows high order "predicates".
But basically high order programming is the idea that you can pass a function around like a variable. Surprisingly a lot of the scripting languages have functions as first class objects as well. LISP/Prolog are a given as AI languages. But some of the others might be surprising. I have seen several AI books for Python. One of them is http://www.nltk.org/book. Also I have seen some for Ruby and Perl. If you study more about LISP you will recognize a lot of its features are similar to modern scripting languages. However LISP came out in 1958...so it really was ahead of its time.
There are AI libraries for Java. And in Java you can sort of hack functions as first class objects using methods on classes, it is harder/less convenient than LISP but possible. In C and C++ you have function pointers, although again they are much more of a bother than LISP.
Once you have functions as first class objects, you can program much more generically than is otherwise possible. Without functions as first class objects, you might have to construct sum(array), product(array) to perform the different operations. But with functions as first class objects you could compute accumulate(array, +) and accumulate(array, *). You could even do accumulate(array, getDataElement, operation). Since AI is so ill defined that type of flexibility is a great help. Now you can build much more generic code that is much easier to extend in ways that were not originally even conceived.
And Lambda (now finding its way all over the place) becomes a way to save typing so that you don't have to define every function. In the previous example, instead of having to make getDataElement(arrayelement) { return arrayelement.GPA } somewhere you can just say accumulate(array, lambda element: return element.GPA, +). So you don't have to pollute your namespace with tons of functions to only be called once or twice.
If you go back in time to 1958, basically your choices were LISP, Fortran, or Assembly. Compared to Fortran LISP was much more flexible (unfortunately also less efficient) and offered much better means of abstraction. In addition to functions as first class objects, it also had dynamic typing, garbage collection, etc. (stuff any scripting language has today). Now there are more choices to use as a language, although LISP benefited from being first and becoming the language that everyone happened to use for AI. Now look at Ruby/Python/Perl/JavaScript/Java/C#/and even the latest proposed standard for C you start to see features from LISP sneaking in (map/reduce, lambdas, garbage collection, etc.). LISP was way ahead of its time in the 1950's.
Even now LISP still maintains a few aces in the hole over most of the competition. The macro systems in LISP are really advanced. In C you can go and extend the language with library calls or simple macros (basically a text substitution). In LISP you can define new language elements (think your own if statement, now think your own custom language for defining GUIs). Overall LISP languages still offer ways of abstraction that the mainstream languages still haven't caught up with. Sure you can define your own custom compiler for C and add all the language constructs you want, but no one does that really. In LISP the programmer can do that easily via Macros. Also LISP is compiled and per the programming language shootout, it is more efficient than Perl, Python, and Ruby in general.
Prolog basically is a logic language made for representing facts and rules. What are expert systems but collections of rules and facts. Since it is very convenient to represent a bunch of rules in Prolog, there is an obvious synergy there with expert systems.
Now I think using LISP/Prolog for every AI problem is not a given. In fact just look at the multitude of Machine Learning/Data Mining libraries available for Java. However when you are prototyping a new system or are experimenting because you don't know what you are doing, it is way easier to do it with a scripting language than a statically typed one. LISP was the earliest languages to have all these features we take for granted. Basically there was no competition at all at first.
Also in general academia seems to like functional languages a lot. So it doesn't hurt that LISP is functional. Although now you have ML, Haskell, OCaml, etc. on that front as well (some of these languages support multiple paradigms...).
The main calling card of both Lisp and Prolog in this particular field is that they support metaprogramming concepts like lambdas. The reason that is important is that it helps when you want to roll your own programming language within a programming language, like you will commonly want to do for writing expert system rules.
To do this well in a lower-level imperative language like C, it is generally best to just create a separate compiler or language library for your new (expert system rule) language, so you can write your rules in the new language and your actions in C. This is the principle behind things like CLIPS.
The two main things you want are the ability to do experimental programming and the ability to do unconventional programming.
When you're doing AI, you by definition don't really know what you're doing. (If you did, it wouldn't be AI, would it?) This means you want a language where you can quickly try things and change them. I haven't found any language I like better than Common Lisp for that, personally.
Similarly, you're doing something not quite conventional. Prolog is already an unconventional language, and Lisp has macros that can transform the language tremendously.
What do you mean by "AI"? The field is so broad as to make this question unanswerable. What applications are you looking at?
LISP was used because it was better than FORTRAN. Prolog was used, too, but no one remembers that. This was when people believed that symbol-based approaches were the way to go, before it was understood how hard the sensing and expression layers are.
But modern "AI" (machine vision, planners, hell, Google's uncanny ability to know what you 'meant') is done in more efficient programming languages that are more sustainable for a large team to develop in. This usually means C++ these days--but it's not like anyone thinks of C++ as a good language for AI.
Hell, you can do a lot of what was called "AI" in the 70s in MATLAB. No one's ever called MATLAB "a good language for AI" before, have they?
Functional programming languages are easier to parallelise due to their stateless nature. There seems to already be a subject about it with some good answers here: Advantages of stateless programming?
As said, its also generally simpler to build programs that generate programs in LISP due to the simplicity of the language, but this is only relevant to certain areas of AI such as evolutionary computation.
Edit:
Ok, I'll try and explain a bit about why parallelism is important to AI using Symbolic AI as an example, as its probably the area of AI that I understand best. Basically its what everyone was using back in the day when LISP was invented, and the Physical Symbol Hypothesis on which it is based is more or less the same way you would go about calculating and modelling stuff in LISP code. This link explains a bit about it:
http://www.cs.st-andrews.ac.uk/~mkw/IC_Group/What_is_Symbolic_AI_full.html
So basically the idea is that you create a model of your environment, then searching through it to find a solution. One of the simplest to algorithms to implement is a breadth first search, which is an exhaustive search of all possible states. While producing an optimal result, it is usually prohibitively time consuming. One way to optimise this is by using a heuristic (A* being an example), another is to divide the work between CPUs.
Due to statelessness, in theory, any node you expand in your search could be ran in a separate thread without the complexity or overhead involved in locking shared data. In general, assuming the hardware can support it, then the more highly you can parallelise a task the faster you will get your result. An example of this could be the folding#home project, which distributes work over many GPUs to find optimal protein folding configurations (that may not have anything to do with LISP, but is relevant to parallelism).
As far as I know from LISP is that is a Functional Programming Language, and with it you are able to make "programs that make programs. I don't know if my answer suits your needs, see above links for more information.
Pattern matching constructs with instantiation (or the ability to easily construct pattern matching code) are a big plus. Pattern matching is not totally necessary to do A.I., but it can sure simplify the code for many A.I. tasks. I'm finding this also makes F# a convenient language for A.I.
Languages per se (without libraries) are suitable/comfortable for specific areas of research/investigation and/or learning/studying ("how to do the simplest things in the hardest way").
Suitability for commercial development is determined by availability of frameworks, libraries, development tools, communities of developers, adoption by companies. For ex., in internet you shall find support for any, even the most exotic issue/areas (including, of course, AI areas), for ex., in C# because it is mainstream.
BTW, what specifically is context of question? AI is so broad term.
Update:
Oooops, I really did not expect to draw attention and discussion to my answer.
Under ("how to do the simplest things in the hardest way"), I mean that studying and learning, as well as academic R&D objectives/techniques/approaches/methodology do not coincide with objectives of (commercial) development.
In student (or even academic) projects one can write tons of code which would probably require one line of code in commercial RAD (using of component/service/feature of framework or library).
Because..! oooh!
Because, there is no sense to entangle/develop any discussion without first agreeing on common definitions of terms... which are subjective and depend on context... and are not so easy to be formulate in general/abstract context.
And this is inter-disciplinary matter of whole areas of different sciences
The question is broad (philosophical) and evasively formulated... without beginning and end... having no definitive answers without of context and definitions...
Are we going to develop here some spec proposal?