I’ve been doing a lot of stuff lately with data structures and algorithms. This is one that seemed remarkably complicated to me – and still kinda does – when I started. It’s not as trivial as a binary tree mainly because there are far fewer rules. There’s a whole lot of stuff you can do with graphs, and this is by no means an advanced or complicated usage of them. This is as simple a task you can do with a graph.
The task at hand is to find all possible paths between two vertices. Continue reading Gist of the Day: Find All Paths for an Undirected Graph
Here’s the scenario: you have two lists of integers, and you have a sum. Write an algorithm – in Perl, of course – to find which two items when summed give you a specified number. If no pairing exists, don’t return anything. If a pairing does exist, return the first pair you encounter. Remember, you could get two very large input lists. Continue reading Gist of the Day: Find Which List Items Make Up a Given Sum
Before you begin the well-deserved flaming, please understand that I do not intend on submitting this to CPAN. This is purely an academic exercise to demonstrate the algorithm (poorly), and to help me practice some stuff. I understand that this isn’t terribly efficient, and I am quite confident that one could do this much faster (and probably has many times over). I wrote this for fun, and for boredom, and I’m posting it because I thought it was cool and because I promised you I’d post stuff.
This algorithm is pretty simple. The premise is that you take a key value of some sort, you hash it using any algorithm, and then you take the mod of that hash value by the capacity of your hash table, and that gives you an index! It’s pretty simple, and even in the implementation I have here I believe it’s not a terrible look-up time.
The basic idea is this:
$array_index = hashval($key) % $array_capacity
Continue reading Gist of the Day: Re-Inventing Hashes… In Perl.
In my post on
Inline::C a few days ago I mentioned The Rules of Optimization Club, and then I ranted a little bit about how if you cannot measure a performance problem then you don’t have a performance problem. That’s not to say that you’re incorrect in asserting that you have a performance problem, it is only to say that you cannot identify any particular part of the problem as a performance problem until you have measured it.
There a great number of ways to measure performance problems, profiling probably being the most useful in situations involving large applications where you want to test performance under real-life situations. For profiling, I would recommend you look at
Devel::NYTProf. This profiler is exceptionally feature-rich and has a boatload of useful functionality. All that said, here I’m only going to talk about bench-marking small pieces of code. Continue reading Gist of the Day: Analyzing Performance with Benchmark.pm