Overlap Detection in strings, with code(neil.fraser.name)

16 pointsby ColinWrightvor 14 Jahren9 comments

neil.fraser.name

http://neil.fraser.name/news/2010/11/04/?

11 comments

kilburnvor 14 Jahren

The algorithms used in this article are _extremely_ bad for the case! Realize that the guy is just trying to find the _longest overlap_ between two strings. This is, the longest part at the end of text1 that is repeated exactly at the beggining of text2.

With that in mind, it is _obvious_ that there is an algorithm linear in the size of the overlap to compute it. For instance:

  def commonOverlapSanely(text1, text2):
  	right = len(text1)-1
  	left  = 0
  	maxleft = len(text2)
  	while left < maxleft and right >= 0:
  		if text1[right] != text2[left]:
  			break
  		right -= 1
  		left += 1
  	return left

Instead of using this _very simple_ algorithm, the author employs three different approaches:

1. A "naive" version that keeps copying increasingly large portions of the original strings, and checking the whole portion each time. This obviously makes the algorithm quadratic in the size of the overlap, thus leading to very bad performance.

2. The KMP algorithm, that is designed to search for occurrences of a string within another one, disregarding where. This is a silly thing to do because the resulting algorithm is linear in the size of the longest input string, instead of on the size of the actual overlap.

3. An "indexOf" monstruosity, that is so wrong it is even hard to explain. Carefuly inspect the code and cry.

Now, please tell me if I missed something...

ColinWrightvor 14 Jahren

OK, as requested: You've missed something.

I've just run a few naive tests and all three versions on the linked page give the same result on every test, and yours gives different results on some of the tests. In short, your code doesn't work.

So you've missed something.

[deleted]vor 14 Jahren

ColinWrightvor 14 Jahren

I see you asked, but then deleted, which I can understand. But to answer your question, "obvious" and "obvious" should return 7, but your code returns 0.

archangel_onevor 14 Jahren

I would have found the article much better if there was more discussion of the "highly efficient indexOf function". It sidesteps the whole discussion of algorithms to appeal to some magic black box with no explanation of how it works - in this case it seems like the speed advantage might very well be because that's implemented in C rather than his KMP implementation which is all in Python. That's still a valid reason to use the approach instead if it means the result is faster, but I'd have preferred to see some more discussion of what was going on behind the scenes.

tomdeakinvor 14 Jahren

This kind of discussion is lacking in the article. It seems as if the OPs goal is optimisation and of course finding the right algorithm with the lowest complexity is the first port of call. However, the indexOf function seems to rely on properties of the data which may not be present (like the forced example in the middle).

There are other algorithms besides KMP too for this. I'm sure with some thinking on my part the linear time suffix tree algorithms could solve this too off hand.

troelsvor 14 Jahren

Agreed. Especially because that would mean that implementing the algorithm at the same level (E.g. in C) would yield much better results than any of the cases.

rflrobvor 14 Jahren

It may be worth pointing out that the OP's appeal to the relevance to biology may not be quite so relevant. Speaking as a bioinformaticist, most of the time we're interested in matching strings with some number of mismatches. In that case, something like the Smith-Waterman algorithm is going to provide much more interesting results. Given the choice between matching 400 basepairs exactly; or 399 basepairs, followed by one mismatch, followed by 399 more basepairs, I'd much rather take the latter case, but the indexOf approach wouldn't be able to tell me that.

cpercivavor 14 Jahren

Also, indels. Mismatches are the easy part -- in my thesis I show how, with linear-time preprocessing, you can solve the overlap problem with a constant proportion of mismatches in O(sqrt(overlap length)) time.

rogerbinnsvor 14 Jahren

A company I worked for was started by someone doing this kind of work in biology. In particular trying to find matches in the ACTG strings and when there is a huge volume of data.

The startup was doing network compression - put an appliance either side of a WAN link and transparently compress the data inbetween. The core loop of that is examining IP and TCP traffic and determining if you have seen something like that before and how much of a match you have. If there is a match then you can just send the match details to the other side rather than the actual data. Typical compression ratios were 20:1 / 95% - ie you could stuff 20 times as much data through the link.

The standard zip algorithm has a window of 32kb (ie it looks roughly 32kb behind to see if data has been repeated). bzip2 uses 900kb. That is usually less than a second or two on standard WAN links! The founder's algorithm worked over hundreds of megabytes.

(The company name was Peribit Networks - you'll find Wikipedia rather unhelpful.)