Top 13 challenge sites

Statusreport in his new blog Ingeration has compiled a list of top challenge sites. I must admit, I already knew some of them in the past and he also told me about some of them earlier. Still, this time around I tried Electrica. Well, it’s really fun solving those puzzles with Python. I’m afraid to start Python Challenge again, after stopping a long time ago; I still have my homework to do!

Algorithms Challenges computer science Programming Python Utility Functions

Small Python Challenge No. 2 – LRU Cache

Caching is easy. Consider the cache I used to optimize the recursive spring:

class _NotInDict(object):
_NotInDict = _NotInDict()
def cached(func):
    cache = {}
    def wrapper_func(*args):
        prev_result = cache.get(args, _NotInDict)
        if prev_result is _NotInDict:
            result = func(*args)
            cache[args] = result
            return result
        return prev_result
    return wrapper_func

This kind of cache is simple and effective (especially for recursions), and may be used in all sorts of situations. However, sometimes you want a size limited cache. In that case you have to decide on the criterion used to decide which items to throw away. There are many kinds of criteria used, for further reading check out wikipedia.

For now I’d like to discuss the LRU cache though. LRU stands for Least Recently Used, which means that you throw away the items you didn’t use for a long time. Time in this case is measured by actions. I thought of this type of cache when I worked on the recursive spring. Since each step in the ‘recursivation’ used two samples of the previous step, caching was an obvious choice, and if I had to size limit my cache, LRU whould be the type of cache to use, as you could be certain that the older samples would not have to be used until the next drawing.

The challenge for the weekend is to write an LRU cache in Python. The cache has to be general – support hash-able keys and any cache size required. It has to be efficient – in the size of the cache and the time it takes for a lookup and an update. Once the standard requirements have been met, the big competition should be on elegance. I wrote a benchmark implementation, which is efficient, but not fast. Once I see some solutions, I’ll talk a bit about mine, which is an interesting case-study.

Algorithms Fractals Programming Python

Optimizing the recursive spring

Remember the recursive spring? Well, when I worked on it, it was quite slow. I mean very slow. Especially at the higher levels.

The original reason was the recursion. Consider this – to ‘springify’ a track, you had to sample it twice, so that you could get the heading. Here is an illustration:

(lower track is ‘inner’ track)

Now it is clear – the number of recursive calls made on all levels grow exponentially. There is some hope. The recursive calls use points calculated previously. It seems that with good caching we’ll be able to cancel out the exponential growth, and make it linear.

Actually, good caching did much more than that – I cached the last track as well. Which meant that the second time around drawing the track didn’t cost anything – just some dictionary lookups. This yielded a pretty elegant result.

To those curious, here is the code. Before I started working on it I hacked together a small 3d explorer with PyOpenGL, so that I’ll be able to play with the 3d models. This module is useful by itself as well, and someday soon I’ll dedicate a post to it. (Only after making it better. Right now I don’t consider it high-quality code.)

To run it, use, and play with it a little. If you look at the code, there are many parameters to play with, yielding interesting results.

Challenges computer science Programming Philosophy Python

A classic programming challenge, in Python

It has become a tradition for computer scientists to create various self referential ‘strange loops’. Traditions such as writing a compiler in the language it compiles are actually quite useful – and also very interesting. This tradition also branched to another one (also mentioned in the linked article) of writing programs that output their own source (without disk access and other dirty tricks).

The challenge is obviously to write such a program in Python, in as few lines as possible. Here is my solution, which is at two lines. I urge you to try it for yourself before looking, it is a very educating challenge. I’ll be very much interested in seeing a one-liner for this problem, or a proof that such a one-liner does not exist.

If you are interested in the bigger challenge, of writing an interpreter for Python in Python itself, go check out PyPy first.

For those interested in other ‘strange loops’, find a copy of ‘Godel Escher Bach’. If you happen to live in Israel, and can come to Haifa, I might even lend you my copy (once I get it back :)

Fractals Graphics Programming Python

Fractals in 10 minutes No. 2 – Recursive Spring

Imagine a straight wire. Bend this wire until its ends meet. You get a ring. Next stage.
Take another straight wire, bend it as before, but this time, don’t let the ends meet, instead continue bending the wire more and more, until you get a spring. Now, Think of this spring as the first wire, and bend it until the spring’s ends meet. We get a spring-ring. (See pictures below)

At this point it should be obvious – instead of making the ends of the spring meet, we can continue bending the spring into a meta-spring, and again make this meta-spring’s ends meet. Rinse and repeat ad-infinitum.

At first I had a hard time picturing it, and I thought it would be tough to write about it without drawing it. So I set with pen and paper, and tried to draw it. That was even harder than picturing it. So I thought I could write a script to draw it, and that turned out to be pretty easy.

Here are the first four stages. The second and third are shown from the side. Click for bigger versions.

To implement this, I wrote a function that draws any track. A track is defined to be a function takes a float argument between 0 and 1, and returns the appropriate 3d point in between, where 0 is one end of the track and 1 is the other. (Actually, I also decided that tracks should return a “right hand” vector as well). Then I wrote a ring:

def ring(t):
    pi = math.pi
    cos = math.cos
    sin = math.sin
    x = numpy.array((2*cos(2*pi*t),2*sin(2*pi*t),0))
    return (x,-x/3)

Lastly I wrote a wrapper function, that takes any track, and springifies it. It was called springify_track. At this point I could do this:
(The constants are the radius of the spring loops, the sampling delta, and the number loops.)

t1 = ring
t2 = springify_track(t1, 0.25, 0.1, 50)
t3 = springify_track(t2, 0.06, 0.0011, 5000)
t4 = springify_track(t3, 0.011, 0.000012, 500000)