Posts tagged "programming":
i've been maintaining xmobar for more than a decade now, and i still use it daily and tweak it almost as often. With almost a hundred contributors besides myself, and many bugs to solve, i am always learning new things. The latest one, that font awesome thing everyone seems so fond of.
i just decided to add a bit more icons to my configuration, and, in particular, to have a battery monitor that displays graphically an approximation of time left.
For that, you specify the new font as one of the additional fonts:
additionalFonts = ["xft:FontAwesome-9"]
Then, find a list of glyphs that represent a charging battery (the
ones between 0xf240 and 0xf244 work well), and assign that to the
flag (the foreground of horizontal bars) with
-W (the display width)
set to 0, and use it in your battery monitor template, which should
<leftbar>; for instance:
BatteryN ["BAT0"] ["-t", "<acstatus>" , "-S", "Off", "-d", "0", "-m", "3" , "-L", "10", "-H", "90", "-p", "3" , "-W", "0" , "-f", "\xf244\xf243\xf243\xf243\xf242\xf242\xf242\xf241\xf241\xf240" , "--" , "-P" , "-a", "notify-send -u critical 'Battery running out!!!!!!'" , "-A", "5" , "-i", "<fn=1>\xf1e6</fn>" , "-O", "<fn=1><leftbar> \xf1e6</fn> <timeleft>" , "-o", "<fn=1><leftbar></fn1> <timeleft>" , "-H", "10", "-L", "7" ] 50 "batt0"
where 0xf1e6 displays a little plug. here's a peek at the corner of my desktop with a charging battery:
The only thing we needed to enable this little feature was to implement indexing on the foreground bar string when the special value 0 is passed, a really small change codewise. Moroever, the feature is generic and applies to any horizontal bar in any monitor, so one can play similar tricks in many places.
This feature will be available in the forthcoming xmobar 0.36 release, but you can of course just grab it from its master branch.
For years, i've been using
C-x o and
C-c <n> to move to other
windows, but with ace window i am substituting all of them with
Problem is, muscle memory interferes and i find myself clumsily moving
around (and often lost) with the former ones. Or i did, before i
followed an advice from Stefan Monnier in emacs-devel: unbind those
keys you want to forget, and you'll get an error when you relapse.
In my case, that was just
(global-set-key (kbd "C-x o") nil)
together with commeting out the other one's definitions. For the
record, in Stefan's case, he was trying to remember to use the newer
C-h o (symbol help) instead of
C-h f (function help) or
(variable help), so he unbound the last two.
After a while, when one's muscles have forgotten, one can re-enable the old bindings, for the few cases where they're justified.
I got started with literate programming many years ago, out of admiration for almost everything else i knew done by Donal Knuth, and tried my hand at it in some toyish projects in OCaml and Scheme. So it wasn't without lack of enthusiasm that i plunged into the literate world.
For reasons i've forgotten (probably better Emacs integration, perhaps simpler syntax) i chose noweb over funnelweb (perhaps, i would recommend the reverse these days, if it weren't for org's babel) and wrote a set of make functions to help create multi-file noweb projects. If memory serves, i was writing an MMIX interpreter in OCaml and a distributed job scheduler in Scheme, and i tried in both cases to use a literate style.
On paper, literate programming is great. Just take a look at Knuth's MMIXware book, Hanson's C Interfaces and Implementations or Jay MacCarthy's blog: that's definitely how i want to read and understand programs for fun and profit.
As i soon learned, that's however not the way i wanted to write programs.
Programming is for me mostly a dialectic process of understanding problems and their solutions, always evolving and always provisional (and most often buggy!), and i seemed never to be able to frozen the solution at hand in a structure that were not in flux. When changing the structure of your solution means rewriting your essay, there's too much friction.
In addition, i tend to build my programs in a bottom-up fashion. Reorganisation comes later, so it's again a chore to start by trying to write down an essay: it's only when i've reached the top that i can see the landscape and properly describe it.
And of course there's the REPL: interactive programming using a live image is, or seemed to be for a long time, at odds with a literate approach to writing your programs.
So, as many others, i just shelved LP for the day i would write a book, and there it's been, untouched, until recently.
It all began when i found myself, more and more often, writing
solutions to customer problems that needed the collaboration of
several programming languages, with a sprinkle of shell scripting.
That need is even more pressing when moreover one routinely uses DSLs
(as we do in BigML with Flatline or WhizzML). Together with all the
programming bits, one needs of course to deliver decent documentation
explaining it. So it's the perfect storm for an org babel document,
specially when one combines it with poly-org. This package allows a
truly poly-mode experience in the org buffer itself: when one enters a
src block, emacs switches to its language's major mode, and one can do
things like sending expressions to the corresponding REPL, or
propertly indenting without having to pop-up a new buffer as in
babel's default modus operandi. It might seem a little thing, but to
me it's made a great difference. Add to that the all but excellent
export capabilities of org, and the easiness with which one can tangle
out different files from the same org file, together with having at
your fingertips the immense gamut of functionality offered by org
mode, and it's very difficult not to become a fan of this form of
Another setting in which babel is lately winning my heart is as a tool for writing emacs lisp packages. It all began with writing my init.el using org, at first tangling it semi-automatically on emacs startup. But then i discovered literate-elisp, a package that teaches emacs to load org files directly, reading the elisp source blocks within, and, most importantly, retaining the source code positions for them. That means that, when one jumps to a function or variable definition1, one lands directly into the org file: no intermediate tangled elisp is needed. That's, again, incredibly handy, specially when combined with poly-org. I can now write packages like signel in an org file that is directly exportable to a blog post, and i find myself writing at the very same time and place both the emacs lisp program and the blog post explaining how it works.
All that said, this new penchant for LP has its obvious limits: i'm aware that it is not going to be comfortable for projects that aren't as self-contained and small as the ones i mention above. For instance, i don't think we could write our clojure backend as a bunch of org files, much as i wish i were wrong.
i've had since forever (it's been so long that i've forgotten
where i stole it) a little utility function that let's me conveniently
jump a la geiser using
M-. to any emacs lisp definiton:
(defun elisp-find-definition (name) "Jump to the definition of the function (or variable) at point." (interactive (list (thing-at-point 'symbol))) (cond (name (let ((symbol (intern-soft name)) (search (lambda (fun sym) (let* ((r (save-excursion (funcall fun sym))) (buffer (car r)) (point (cdr r))) (cond ((not point) (error "Found no definition for %s in %s" name buffer)) (t (switch-to-buffer buffer) (goto-char point) (recenter 1))))))) (cond ((fboundp symbol) (elisp-push-point-marker) (funcall search 'find-function-noselect symbol)) ((boundp symbol) (elisp-push-point-marker) (funcall search 'find-variable-noselect symbol)) (t (message "Symbol not bound: %S" symbol))))) (t (message "No symbol at point"))))
Recently i bought a second-hand copy of Simon Peyton Jones' classic The implementation of functional programming languages, and i've been having some very pleasant reading hours during the last week.
For instance, i've enjoyed Simon's clear and to-the-point introduction to the lambda calculus and, more concretely, his really lucid explanation of the workings of recursion and the Y combinator. As a matter of fact, i've enjoyed it so much that i just wanted to briefly reproduce it here, paraphrased in my surely poorer style, just for the fun of it.
In the pure lambda calculus, there's no direct way of defining a
function that refers to itself: all you have is the ability to define
anonymous lambda abstractions. Using Scheme's syntax, that means that
you can use
lambda as much as you want, as in
((lambda (x) (+ 1 x)) 3)
define (recursive or not) is not available. For instance, you
cannot define a
list-length recursive function like this:
(define list-length (lambda (lst) (if (null? lst) 0 (+ 1 (list-length (cdr lst))))))
the main problem being the appearance of
list-length in the lambda
expression's body. So the general issue we want to solve is how to
define a function
f of the form
f = (... f ...)
without referring to
f in the body. When all you have is lambda
calculus, everything looks like a function: we can rewrite the right
hand side of the above equation as a lambda application:
f = ((lambda (x) (... x ...)) f) := (h f)
where we've defined
h := (lambda (x) (... x ...)). For instance, in
our length example,
h is the function
h = (lambda (x) (lambda (lst) (if (null? lst) 0 (+ 1 (x (cdr lst))))))
and we're trying to solve the equation
(h list-length) = list-length.
In other words, you give me
h, a function taking a function as
argument and returning another function, and i need to give you back an
f that is its fixed point, i.e., an
f = (h f).
So all i need to do is finding a function (let's call it
Y) that takes
h as its argument and returns
f, that is, by definition of
f = (Y h)
but, as we've seen
(h f), which in turn is
(h (Y h))
which gives us the defining equation of
(h (Y h)) = (Y h)
If i had that magic
Y function (or combinator, since it won't use any
global variables), i could for instance immediately compute
(Y (lambda (x) (lambda (lst) (if (null? lst) 0 (+ 1 (x (cdr lst)))))))
The nicest kind of wishful thinking is the one that comes true: we
actually know of a lambda expression that satifies the Y combinator's
(Y h) = (h (Y h)), namely:
(lambda (h) ((lambda (x) (h (x x))) (lambda (y) (h (y y)))))
You can check it by yourself:
(Y h) is just the body of the expression
(Y h) => ((lambda (x) (h (x x))) (lambda (y) (h (y y))))
and now apply the first lambda to its argument (the second lambda) to obtain:
=> (h ((lambda (y) (h (y y))) (lambda (y) (h (y y))))) => (h (Y h))
as desired! The only wrinkle left is that, as defined, our Y
combinator cannot be directly coded in Scheme, because Scheme is a
strict language and those pesky
(x x) and
(y y) applications cause an
But that's just an accidental difficulty due to Scheme's evaluation
strategy being too eager that has a standard solution: a beta
abstraction. If you have a function
F in Scheme, you can define a
totally equivalent function
(define G (lambda (x) (F x))). We say
that G is a beta abstraction of F, or that F is a beta reduction of
G. And the usual reason you would beta abstract a function in Scheme
is in order to delay the evaluation of its body, just what the doctor
h is a function taking another function as an argument, so
(x x) and
(y y), which are used as
h's arguments, must be
functions, so they're equivalent to
(lambda (a) ((x x) a)) and
(lambda (a) ((y y) a)), so we can define Y, if we're so inclined, also
(define Y (lambda (h) ((lambda (x) (h (lambda (a) ((x x) a)))) (lambda (x) (h (lambda (a) ((x x) a)))))))
(define list-length (Y (lambda (f) (lambda (lst) (if (null? lst) 0 (+ 1 (f (cdr lst))))))))
As you see,
list-length's body doesn't mention itself and, as you can
check by yourself with your favourite interpreter, the last two
definitions are valid and working scheme code.
In case you still feel a bit unsure as to how our
Y is pulling its
trick, you can dispel any misgivings just by seeing how the combinator
transforms the given
h step by step, always reproducing
h in the
process (it's only the branch in
h that doesn't call its argument, the
recursion's base case, that breaks the implicit infinite loop induced by
Y). Let's do it with our
list-length example, reducing its
application to, say, the list '(1 2), step by step:
(define W (lambda (f) (lambda (a) ((f f) a)))) (list-length '(1 2)) ;; definition of list-length as (Y h) => ((Y h) '(1 2)) ;; definiton of Y and W => (((lambda (f) (h (W f))) (lambda (f) (h (W f)))) '(1 2)) ;; beta-reduction of the first lambda applied to the second => ((h (W (lambda (f) (h (W f))))) '(1 2)) ;; definition of h for the case of list-length => ((lambda (lst) (if (null? lst) 0 (+ 1 ((W (lambda (f) (h (W f)))) (cdr lst))))) '(1 2)) ;; function application => (+ 1 ((W (lambda (f) (h (W f)))) '(2))) ;; definition of W => (+ 1 (((lambda (f) (h (W f))) (lambda (f) (h (W f)))) '(2)))
and notice what just happened: we started with
(((lambda (f) (h (W f))) (lambda (f) (h (W f)))) '(1 2))
and, after one iteration of the implicit recursion, reduced it to:
(+ 1 (((lambda (f) (h (W f))) (lambda (f) (h (W f)))) '(2)))
i.e., 1 plus the same form we started with but applied to the list
instead of the list
(1). By following the exact same reduction steps,
we can simplify the above to:
(+ 1 1 (((lambda (f) (h (W f))) (lambda (f) (h (W f)))) '()))
and now, when expanding the same form to the right applied to the
empty list, the base case of the recursion (the true branch of the
(null? lst) test in
h), will kick in and reduce the above to
(+ 1 1 0) => 2
avoiding that the self-reproducing Y forms keep reappearing ad infinitum.
For many years, i've been convinced that programming needs to move forward and abandon the Algol family of languages that, still today, dampens the field. And that that forward direction has been signalled for decades by (mostly) functional, possibly dynamic languages with an immersive environment. But it wasn't until recently that i was able to finally put my money where my mouth has been all these years.
A couple of years ago i finally became a co-founder and started working on a company i could call my own (i had been close in the past, but not really there), and was finally in a position to really influence our development decisions at every level. Even at the most sensitive of them all: language choice.
During my previous professional career i had been repeatedly dissapointed by the lack of conviction, when not plain mediocrity, of the technical decision makers in what seemed all kinds of company, no matter how cool, big or small: one would always end up eaten alive by the Java/C++ ogre, with a couple testimonial scripts (or perhaps some unused scheme bindings) to pay lip service to the hipsters around.
Deep down, the people in charge either didn't think languages make any difference or, worse, bought into the silly "availability of programmers" argument. I'm still surprised anyone would believe such a thing. If i guy came telling me that he wanted to program only in, say, Java because that's what he knows best and that he doesn't really feel prepared or interested in learning and using, say, Clojure (or any other language, really), i wouldn't hire him in a million years, no matter what language my project were using, and no matter how many thousands of candidates like this one i had at my disposal.
Give me programmers willing to learn and eager to use Lisp or Haskell (or Erlang, ML, OCaml, Smalltalk, Factor, Scala…) any day, even if we happen to be using C++, for goodness sake! Those are the ones one needs, scanty as they might be.
Given the sad precedents, when i embarked in my new adventure, i was careful not to try to impose on anyone my heretical ideals: they had to be accepted on their own grounds, not based on my authority. But i was finally lucky enough to meet a team of people with enough intellectual curiosity and good sense (which is, again, actually a pre-condition to be in a startup). People, let me tell you, with no experience whatsoever in languages outside the mainstream, but people that, nonetheless, were good programmers. And when you give a good programmer a good language, good things happen.
Come to think of it, it's true that, with good programmers, the language doesn't matter: they'll choose the best one, or follow the advice of more experienced colleagues, and quickly take advantage of any extra power the novel environment has to offer.
Our experience so far could hardly be a better counterexample against algol naysayers.
Our backend is 99.4% coded in Clojure, and 66% of the team had never programmed seriously in any lisp, let alone Haskell or Prolog (heck, not even i (the remaining 33%) had actually tried anything non-mainstream for real in a big project!) Maybe some Ruby, and lots and lots of Java and C and C++. But they accepted the challenge after reading around and learning the basics, and 3 months later you couldn't take Clojure from their prying hands. More importantly, they had fun discovering they could also be Dr Jekyll.
Of course, lots of effort is a must, and someone with a bit of experience to guide the newbies is probably necessary. In particular, extensive code reviews were of the essence in our case, and i had never read and criticised this many lines of code in a similar amount of time. But know what? I prefer to "waste" time in code reviews to employ at least as much writing factories of factories of factories and similar boilerplate (configured of course using XML) and chasing bugs in the resulting soup. Not to mention that, again, you need code reviews no matter the language you use: the trick of having a code base so ugly that nobody would review it doesn't fly.
So yes, it's more difficult to find hackers, but you need to find them anyway. And yes, it may require more serious software engineering, but, again, you need it anyway. So why shouldn't we use the best tools at our disposal? I can finally tell you: been there, done it, and it does work.
Introduction: lists galore
I learned programming backwards, plunging right on into C and, shortly after, C++ and Java from the very beginning. I was knee deep in complex data structures, pointers and abstruse template syntax in no time. And the more complex it all felt, the more i thought i was learning. Of course, i was clueless.
Reading SICP and learning about functional programming changed it all. There were many occasions for revelation and awe, but one of my most vivid recollections of that time is my gradual discovery of the power of simplicity. At about half way into SICP i realised in wonder that that beautiful and powerful world was apparently being constructed out of extremely simple pieces. Basically, everything was a list. Of course there were other important ingredients, like procedures as first-class objects, but the humble list was about the only data structure to be seen. After mulling on it for a little bit, i saw where lists draw their power from: recursion. As you know, lists are data types recursively defined: a list is either the empty list or an element (its head) followed by another list (its tail):
list = 
list = a : list
where i'm borrowing Haskell's notation for the empty list () and the list constructor (:), also known by lispers as () and cons. So that was the trick, i thought: lists have recursion built-in, so to speak, and once you've read a little bit about functional programming you don't need to be sold on the power and beauty of recursive programs.
It is often the case that powerful and beautiful yet simple constructs have a solid mathematical foundation, and only when you grasp it do you really realize how powerful, beautiful and amazingly simple that innocent-looking construct is. Lists, and recursive operations on them, are an excellent case in point. But the path connecting them to their mathematical underpinnings is a long and winding one, which lays in the realm of Category Theory.
I first became acquainted of the relationship between categories and recursive programming reading Functional Programming with Bananas, Lenses, Envelopes and Barbed Wire, by Erik Meijer, Maarten Fokkinga and Ross Paterson. Albeit very enjoyable, this paper presupposes a high degree of mathematical sophistication on the reader's side. I will try in this article to give you a simplified overview of the concepts involved, including Category Theory, its application to programming languages and what funny names like catamorphism, anamorphism or lambda-lifting have to do with your everyday list manipulations. Of course, i'll be only scratching the surface: interspersed links and the Further reading section provide pointers to more in-depth explorations of this wonderland.
Categories are (relatively) simple constructs. A category consists of a set O of objects, and a set A of arrows between elements of O. Arrows are composable: if there's an arrow from a to b, and another one from b to c, there must be an arrow from a to c (where a, b and c are elements of O). Besides, they are associative: if you have arrows from a to b, b to c, and c to d, you can go from a to d via two different paths, namely, first from a to c and then from c to d, or first from a to b and then from b to d. Finally, for each element a in O there's an identity arrow which goes from a to itself (called an identity), such that following this arrow changes nothing. These properties are better visualized with a diagram (or a bit of mathematical notation), as shown in the image on the right.
A category captures a mathematical world of objects and their relationships. The canonical example of a category is Set, which contains, as objects, (finite) sets and, as arrows, (total) functions between them. But categories go far beyond modeling sets. For instance, one can define a category whose objects are natural numbers, and the 'arrows' are provided by the relation "less or equal" (that is, we say that there is an arrow joining two numbers a and b if a is less or equal than b)/./ What we are trying to do with such a definition is to somehow capture the essence of ordered sets: not only integers are ordered but also dates, lemmings on a row, a rock's trajectory or the types of the Smalltalk class hierarchy. In order to abstract what all those categories have in common we need a way to go from one category to another preserving the shared structure in the process. We need what the mathematicians call an isomorphism, which is the technically precise manner of stating that two systems are, in a deep sense, analogous; this searching for commonality amounts to looking for concepts or abstractions, which is what mathematics and (good) programming is all about (and, arguably, intelligence itself, if you are to believe, for instance, Douglas Hofstadter's ideas).
To boot, our definition of a category already contains the concept of isomorphic objects. Think of an arrow from a to b as an operation that transforms a in b. An arrow from b to a will make the inverse transformation. If composing both transformations gives you the identity, you are back to the very same object a, and we say that a and b are isomorphic: you can transform one into the other and back at will. In a deep sense, this concept captures a generic way of expressing equality that pervades all maths: if you're not afraid of a little bit of maths, Barry Mazur's essay When is a thing equal to some other thing? is an excellent introduction to Category Theory with an emphasis in the concept of equality. Among many other things, you will learn how the familiar natural numbers can be understood as a category, or how an object is completely defined by the set of its transformations (and, therefore, how to actually get rid of objects and talk only of transformations; i know this is stretching and mixing metaphors (if not plain silly), but this stress in arrows, as opposed to objects, reminded me of Alan Kay's insistence on focusing on messages rather than objects). Another introductory article with emphasis on categories as a means to capture sameness is R. Brown and T. Porter's Category Theory: an abstract setting for analogy and comparison.
Not only objects inside a category can be transformed into each other. We reveal the common structure of two disjoint categories by means of a functor mapping across two categories. A functor consists of two functions: one that maps each object of the first category to an object in the second, and another one putting in correspondence arrows in one category with arrows in the second. Besides, these functions must preserve arrow composition. Let me spell this mathematically. Consider to categories, C and C' with object sets O and O' and arrow sets A and A'. A functor F mapping C to C' will consist then of two functions (Fo, Fa); the first one taking elements of O to elements of O':
Fo: O -> O'
Fo(a) in O' for every a in O
and the second one taking arrows from A to arrows in A':
Fa: A -> A'
Fa(f) in A' for every f in A
and such that, if f is an arrow from a to b in C, Fa(f) is an arrow from Fo(a) to Fo(b) in C'. Moreover, we want that following arrows in C is 'analogous' to following them in C', i.e., we demand that
Fa(fg) = Fa(f)Fa(g)
In the left hand side above, we are composing two arrows in C and then going to C', while in the right hand side we first take each arrow to C' and, afterwards, compose them in there. If C and C' have the same structure, these two operations must be equivalent. Finally, F must preserve identities: if i is the identity arrow for an element a in O, Fa(i)must be the identity arrow for Fo(a) in O'. The diagram on the left shows a partial graph (i'm not drawing the identity arrows and their mappings) of a simple functor between two categories, and ways of going from an object a in the first category to an object x in the second one which are equivalent thanks to the functor's properties.
As you can see in this simple example, the functor gives us the ability of seeing the first category as a part of the second one. You get a category isomorphism in the same way as between objects, i.e., by demanding the existence of a second functor from C' to C (you can convince yourself that such a functor does not exist in our example, and, therefore, that the two categories in the diagram are not isomorphic).
You have probably guessed by now one nifty property of functors: they let us going meta and define a category whose objects are categories and whose arrows are functors. Actually, Eilenberg and MacLane's seminal paper General theory of natural transformations used functors and categories of categories to introduce for the first time categories (natural transformations are structure-preserving maps between functors: this Wikipedia article gives an excellent overview on them).
But enough maths for now: it is high time to show you how this rather abstract concepts find their place in our main interest, programming languages.
Categories and programming languages
About the only similarity between C and Haskell programming is that one spends a lot of time typing ASCII arrows. But of course, Haskell's are much more interesting: you use them to declare the type of a function, as in
floor:: Real -> Int
The above stanza declares a function that takes an argument of type real and returns an integer. In general, a function taking a single argument is declared in Haskell following the pattern
fun:: a -> b
where a and b are types. Does this ring a bell? Sure it does: if we identify Haskell's arrows with categorical ones, the language types could be the objects of a category. As we have seen, we need identities
id:: a -> a
id x = x
and arrow composition, which in Haskell is denoted by a dot
f:: b -> c
g:: a -> b
fg:: a -> b -> c
fg = f . g
Besides, associativity of arrow composition is ensured by Haskell's referential transparency (no side-effects: if you preserve referential transparency by writing side-effect free functions, it won't matter the order in which you call them): we've got our category. Of course, you don't need Haskell, or a statically typed language for that matter: any strongly typed programming language can be modelled as a category, using as objects its types and as arrows its functions of arity one. It just happens that Haskell's syntax is particularly convenient, but one can define function composition easily in any decent language; for instance in Scheme one would have
(define (compose f g) (lambda (x) (f (g x)))
Functions with more than one arguments can be taken into the picture by means of currying: instead of writing a function of, say, 2 arguments:
(define (add x y) (+ x y))
(add 3 4)
you define a function which takes one argument (x) and returns a function which, in turn, takes one argument (y) and returns the final result:
(define (add x) (lambda (y) (+ x y)))
((add 3) 4)
Again, Haskell offers a pretty convenient syntax. In Haskell, you can
add x y = x + y
which gets assigned, when applied to integers, the following type:
add:: Int -> (Int -> Int)
add is not a function from pairs of integers to integers, but
a function that takes an integer and returns a function of type
Int -> Int.
Finally, we can also deal with functions taking no arguments and
constant values by introducing a special type, called unit or 1 (or
void in C-ish), which has a unique value (spelled
Haskell). Constants of our language (as, e.g.,
True or 43.23) are then
represented by arrows from 1 to the constant's type; for instance,
True is an
1 -> Boolean arrow. The unit type is an example of what in
category theory is known as a terminal object.
Now that we have successfully modelled our (functional) programming
language as a category (call it C), we can use the tools of the theory
to explore and reason about the language constructs and
properties. For instance, functors will let me recover the original
motivation of this post and explore lists and functions on them from
the point of view of category theory. If our language provides the
ability to create lists, its category will contain objects (types) of
the 'list of' kind; e.g.
[Int] for lists of integers,
lists of Booleans and so on. In fact, we can construct a new
sub-category CL by considering list types as its objects and functions
taking and returning lists as its arrows. For each type a we have a
way of constructing a type, [a] in the sub-category, i.e., we have a
map from objects in C to objects in CL. That's already half a
functor: to complete it we need a map from functions in C to functions
in CL. In other words, we need a way to transform a function acting on
values of a given type to a function acting on lists of values of the
same type. Using the notation of the previous section:
Fo(a) = [a]
Fa(f: a -> b) = f': [a] -> [b]
Fa is better known as
map in most programming languages. We call the
process of going from
f' lifting (not to be confused with a
related, but not identical, process known as lambda lifting), and it's
usually pretty easy to write an operator that lifts a function to a
new one in CL: for instance in Scheme we would write:
(define (lift f) (lambda (lst) (map f lst)))
lift to truly define a functor we need that it behaves well
respect to function composition:
(lift (compose f g)) = (compose (lift f) (lift g))
We can convince ourselves that this property actually holds by means of
a simple example. Consider the function
next which takes an integer to
its successor; its lifting
(lift next) will map a list of integers to
a list of their successors. We can also define
mapping (lists of) integers to (lists of) their predecessors.
(compose next prev) is just the identity, and, therefore,
(lift (compose next prev)) is the identity too (with lifted
signature). But we obtain the same function if we compose
(lift prev) in CL, right? As before, there's nothing specific to
Scheme in this discussion. Haskell even has a
Functor type class
capturing these ideas. The class defines a generic lift operation,
fmap that, actually, generalizes our list lifting to arbitrary
fmap :: (a -> b) -> (f a -> f b)
f a is the new type constructed from
a. In our previous
f a = [a], but if your language gives you a way of
constructing, say, tuples, you can lift functions on given types to
functions on tuples of those types, and repeat the process with any
other type constructor at your disposal. The only condition to name it a
functor, is that identities are mapped to identities and composition is
fmap id = id
fmap (p . q) = (fmap p) . (fmap q)
I won't cover usage of type constructors (and their associated functors) other than lists, but just mention a couple of them: monads, another paradigmatic one beautifully (and categorically) discussed by Stefan Klinger in his Programmer's Guide to the IO Monad - Don't Panic (also discussed at LtU), and the creation of a dance and music library, for those of you looking for practical applications.
To be continued…
Returning to lists, what the lifting and categorical description above buys us is a way to formalize our intuitions about list operations, and to transfer procedures and patterns on simple types to lists. In SICP's section on Sequences as conventional interfaces, you will find a hands-on, non-mathematical dissection of the basic building blocks into which any list operation can be decomposed: enumerations, accumulators, filters and maps. Our next step, following the bananas article i mentioned at the beginning, will be to use the language of category theory to provide a similar decomposition, but this time we will talk about catamorphisms, anamorphisms and similarly funny named mathematical beasts. What the new approach will buy us is the ability to generalize our findings beyond the list domain and onto the so-called algebraic types. But this will be the theme of a forthcoming post. Stay tunned.
The best introductory text on Category Theory i've read is Conceptual Mathematics : A First Introduction to Categories by F. William Lawvere (one of the fathers of Category Theory) and Stephen Hoel Schanuel. It assumes no sophisticated mathematical background, yet it covers lots of ground. If you feel at home with maths, the best option is to learn from the horse's mouth and get a copy of Categories for the Working Mathematician, by Mac Lane.
The books above do not deal with applications to Computer Science, though. For that, the canonical reference is Benjamin Pierce's Basic Category Theory for Computer Scientists, but i find it too short and boring: a far better choice is, in my opinion, Barr and Well's Category Theory and Computer Science. A reduced version of Barr and Well's book is available online in the site for their course Introduction to Category Theory. They are also the authors of the freely available Toposes, Triples and Theories, which will teach you everything about monads, and then more. Marteen Fokkinga is the author of this 80-pages Gentle Introduction to Category Theory, with a stress on the calculational and algorithmical aspects of the theory. Unless you have a good mathematical background, you should probably take gentle with a bit of salt.
Let me close by mentioning a couple of fun applications of Category Theory, for those of you that know a bit about it. Haskell programmers will like this implementation of fold and unfold (as a literate program) using F-(co)algebras and applied to automata creation, while those of you with a soft spot for Physics may be interested in John Baez's musings on Quantum Mechanics and Categories.
… but not simpler.
Einstein's (attributed) quotation has become an aphorism, taken for granted by every mathematician or physicist i've ever met (to mention two kinds of people i've been frequently involved with). One would expect the same attitude from a community that invented the term 'no silver bullet', and yet, since i got into computer science, first for fun and later on for a living, i've found lots of people with, er, a different viewpoint. Take for instance this excerpt from Lisp is sin, a widely cited and commented article by Sriram Krisnan:
In Visual Studio, we look at 3 distinct categories of programmers. We call them Mort, Elvis and Einstein - the 3 personas we take into consideration whenever we create any developer technology. What is difficult for some of us geeks to comprehend sometimes is - all 3 of them are equally important. When you look at technology like Windows Workflow Foundation, one of the driving forces was to let non-geeks build software. Not everyone needs to be a Raymond Chen or a Dave Cutler. Not everyone needs to understand the difference between the various GC algorithms. However, everyone needs the ability to be productive. And everyone needs to be able to get software working without needing a CS degree.
We cannot afford to restrict software development only to those who know Windows kernel internals or those who can understand what a continuation is. It's not that other people are not smart - they just have better things to do. That's the key piece of understanding I find missing sometimes.
Nonsense, if you ask me. And yet, i've been hearing this same argument, in different dressings, once and again since i got into computer science. Let's apply the same line of reasoning to other disciplines, and see how well it fares:
Hey Albert, your General Relativity is awesome but, you know, with all that jazz about differential geometry and curved spacetimes, it's too hard; we're not as smart as you, pal, so we'd better use Newtonian or Aristotelian mechanics to calculate those GPS satellite orbits and get going with other important things we need to do. Hope you understand, Albert.
Well Santiago, your ideas about neurons and surgery sound pretty deep and mystifying, but please, think of the Galens among us: we don't have the time to investigate every new fad, and, anyway, we wouldn't understand it if we did. Know what? We'll keep using our old good cures and stay away from untrodden venues. Our healing parchments are a bit of a hack, but they get the work done… most of the time, that is.
Does it make any sense? Now, maybe you think that i am exaggerating, and that the comparisons above are stretching the point a bit too far. If so, take a second to look back to the people that made your nice computing environment possible. Take a look at Charles Babagge visions; read about Alan Turing and Alonzo Church or John von Neumann; admire the elegance of McCarthy's original LISP (1960); prepare to be surprised with the things the people in Dough Engelbart's Augmentation Research Center were doing during the sixties; try to find a modern drawing program that matches Sketchpad's algorithms (or see it in action in this presentation by Alan Kay); follow the fascinating development of the overlapping windows interface, hand in hand with Smalltalk history back at Xerox PARC, and do it from the horse's mouth; feel the thrill of the people that went beyond Xerox's big wigs' shortsightedness and on to making a dent in the universe: it was 1984, that same year the lisp machine wars culminated in the creation of the GNU project, which was all about ideals, about empowering people, about freedom. When you're done, tell me whether i'm going overboard when making parallelisms between computer science and physics or medicine!
All those people had a vision, a dream, and pursued it with an amazing display of hard work, stubbornness and intelligence. They took no prisoners, and by the late eighties had pushed that new thing called, for want of a better name, Computer Science to its modern standards.
Then winter came. Not just the AI winter. Compare the swift pace of CS developments during the 1960-80 period with the subsequent advancements in the field. We're using the same metaphors, the same kind of systems that we inherited from those guys and gals. Why, we even envy the power of Lisp Machines these days. It's been a long, cold winter for CS. And the main reason was the appearance of the mentality that i'm criticising in this post, what Alan Kay aptly calls, in a recent interview, a pop culture of computers:
Perhaps it was commercialization in the 1980s that killed off the next expected new thing […] But a variety of different things conspired together, and that next generation actually didn't show up. One could actually argue—as I sometimes do—that the success of commercial personal computing and operating systems has actually led to a considerable retrogression in many, many respects. You could think of it as putting a low-pass filter on some of the good ideas from the '60s and '70s, as computing spread out much, much faster than educating unsophisticated people can happen. In the last 25 years or so, we actually got something like a pop culture, similar to what happened when television came on the scene and some of its inventors thought it would be a way of getting Shakespeare to the masses. But they forgot that you have to be more sophisticated and have more perspective to understand Shakespeare. What television was able to do was to capture people as they were. So I think the lack of a real computer science today, and the lack of real software engineering today, is partly due to this pop culture.
Dead on, i say. People advocating about making programming simpler than possible are the hallmark of this pop culture. And when corporate and economic interests enter the picture, things get even worse. The Lisp is sin essay goes on to say:
I frequently see on Slashdot "Windows is designed for stupid users". That is quite insulting to the millions of moms and dads, teachers and laywers and people from other walks of life who use Windows or even the Mac. If we mandated that every new user understand Windows' command line syntax or Emacs, we would have failed as an industry - we would have locked out the rest of the world.
In my opinion, this totally misses the point. There's nothing wrong in making computers simpler to users. On the contrary, that's probably what this endeavour is all about. Alan Kay saw it, Apple took head with its computer for the rest of us mantra. But it does not follow that there must be a CS for the rest of us. Making all this amazing technology possible takes effort, and needs a high level of sophistication. Alan didn't try to create systems usable by children inventing PHP. He created Smalltalk striving to improve Lisp, he studied Piaget and Papert, he has degrees in maths and biology. And he needed all that, and then more.
The (trivial) point i'm trying to make is that not everybody has what it takes to be a programmer. Just as not everybody can be a singer or a painter (as an aside, i tend to agree with the opinions that link programming and art). As a matter of fact, good programmers are rare and need a quite peculiar combination of skills and talents. Donald Knuth has put it far better than i could in the essay Theory and Practice, II (from his Selected Papers on Computer Science):
The most important lesson [after developing TeX], for me, was that software is hard; and it takes a long time. From now on I shall have significantly greater respect for every successful software tool that I encounter.[…] Software creation not only takes time, it's also much more difficult that I thought it would be. Why is this so? I think the main reason is that a longer attention span is needed when working on a large computer program than when doing other intellectual tasks. A great deal of technical information must be kept in one's head, all at once, in high-speed random-access memory somewhere in the brain.
We don't solve the painter's problem by complaining that perspective is hard to grasp and people should better use flat icons. In the same way, we shouldn't be claiming for a trivialisation of CS both in academia and in the industry. The we would have failed in the industry bit in the Sriram quote above is really sad: we're sacrificing an admirable legacy in the name of industry and corporate profit. The most remarkable feat of our current industry leaders is to have convinced the rest of the world that having software systems that eat incredible amounts of resources and explode without reason every now and then is part of an acceptable, even top-notch, technology. Fortunately, other disciplines show far more respect for the people that, ultimately, is paying their wages.
If you've got this far, you already have one of the qualities needed to become a programmer: stamina. You'll need more. Be prepared to study hard, to learn maths, to live in abstract worlds. If you feel that you have "more important things to do", well, that's all very well, but don't ask the rest of us to dumb down the subject so that everybody can be a programmer. Lisp is not a sin. The sin would be to betray the dreams, ideals and hard work of the people that have taken us this far. We owe that to them, and to ourselves.
To end this never-ending diatribe, let me add a couple of things: first, i should apologize for taking Sriram as the scapegoat to a long honed rage: his essay contains many good points worth reading and mulling on; second, i hope you're not thinking this is just an arrogant rant by an old fart: i'm not that old.