| --- Log opened Mon Apr 26 00:00:19 2010 |
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| 03:09 | < Orth> | Hum. OK: Could someone explain Completeness In A Nutshell? |
| 03:10 | < Orth> | I know the general concept, but I'm trying to come up with a quick bulletpoint list of the different terms. (And I suck at compression; I prefer to ramble until I miss the point~) |
| 03:14 | < Finerty> | "completeness"? |
| 03:14 | <@McMartin> | "if you're $FOO-Complete, any problem in $FOO is exactly as hard as you, because you can phrase that problem in terms of yourself." |
| 03:16 | < Finerty> | That? Or something esle. |
| 03:16 | < Orth> | Yeah |
| 03:16 | < Orth> | NP-Complete, the not-NP-one-I-forget, NP-Hard. |
| 03:17 | < Finerty> | there's P, NP, NP-Complete, and NP-Hard, are the ones that are mostly talked about. |
| 03:18 | < Finerty> | P: "Easy" problems. Ones where the solution can be discovered in a number of steps proportional to some constant power of the amount of information in the problem. |
| 03:19 | < Finerty> | Examples include stuff like matrix multiplication, sorting, and primality testing with modern setups. |
| 03:20 | < Finerty> | NP: "Easy to check" problems. Ones where the solution may not be easy to find, but once a candidate solution has been found, it's easy to tell whether it's right or not. |
| 03:22 | < Finerty> | Examples include prime factorization (because once you /have/ a solution all you have to do is multiply to check it). |
| 04:13 | < Orth> | Awesome, thank you. (That helped a lot actually; I hadn't comprehended that that's what made something NP.) |
| 04:14 | < Orth> | NP-Hard is when it's hard to tell if you got it right, even once you've done it, yah? |
| 04:16 | < Finerty> | No. |
| 04:17 | < Finerty> | NP-Hard means that it is at least as hard as every other NP problem -- I... actually don't know the difference betweeen NP-Hard and NP-Complete. |
| 04:17 | < Finerty> | Ah, I see. |
| 04:18 | < Finerty> | NP-Complete is those problems that are both NP-Hard /and/ NP. Specifically, NP-Hard problems are not necessarily NP! |
| 04:23 | < Finerty> | THere are many kinds of non-NP problems. |
| 04:24 | < Orth> | Huh. Okay. |
| 04:26 | < Finerty> | generally they're harder -- they're the way you tried to describe NP-hard problems. |
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| 18:13 | | * Derakon mutters at py2app. |
| 18:14 | <@Derakon> | This is a Python module that's supposed to create OSX-style applications out of Python scripts. |
| 18:14 | <@Derakon> | Only problem is that it refuses to include the actual Python executable if you run it with the built-in OSX Python. So naturally the "applications" it makes work only on the computer in which they were created, or other computers with identical Python setups. *sigh* |
| 18:15 | <@Derakon> | The solution is to install a second Python and use that to make the app. |
| 18:15 | <@Derakon> | There appears to be no "no really, include the OS-default Python in this app" option. |
| 18:24 | <@Derakon> | (This also explains why I didn't have problems with py2app before; my old computer's OS-default Python was 2.4, way too old for my purposes) |
| 18:58 | | * Derakon eyes his attempted scipy install: "error: library dfftpack has Fortran sources but no Fortran compiler found" |
| 18:59 | <@Derakon> | I keep forgetting that people still use Fortran. |
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| 19:36 | <@McMartin> | It's still easier to parallelize than the ALGOL-descended languages |
| 19:39 | <@Derakon> | Mental note: applications will load faster if they don't have to run off of a USB thumb drive. |
| 19:39 | <@Derakon> | This is particularly true when the application consists in large part of ~120MB of libraries. |
| 19:40 | <@McMartin> | Heh |
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| 20:46 | <@McMartin> | Bravo, Ubuntu |
| 20:46 | | * McMartin tries to look at the RC, gets a redirect loop. |
| 20:48 | < gnolam> | Is it time for a new Ubuntu already? |
| 20:49 | <@McMartin> | It's 10/04. |
| 20:56 | | * Derakon mutters at scipy, which is steadfastly refusing to not increase the size of his input array when he calls zoom() on it. |
| 20:56 | <@Derakon> | (My desired result is to truncate data that falls outside the boundaries of the initial array; there appears to be no argument that allows for this) |
| 21:00 | <@Derakon> | (It would also be nice if the arguments to the function were all described in the documentation...many of them appear to be standard arguments for the suite of array transformation functions, but AFAICT these are simply not described anywhere) |
| 21:22 | <@jerith> | One would expect zoom() to make your data whizz off into the distance. |
| 21:23 | <@jerith> | Derakon: I made py2app work properly at one point. |
| 21:23 | <@jerith> | There was probably trickery involved. |
| 21:26 | <@Derakon> | Oh, I got it working. |
| 21:26 | <@Derakon> | It involved installing a second copy of Python 2.5 and reinstalling all the dependencies using that copy. |
| 21:27 | <@Derakon> | In other words, a pain, but a straightforward pain. |
| 21:27 | <@jerith> | Ah. |
| 21:31 | | * Derakon uses scipy's affine_transform function using a straightforward transformation matrix, instead of its zoom() function. |
| 21:31 | <@Derakon> | I suppose this means I should fold the rotational transform into that call... |
| 21:31 | <@Derakon> | It would speed things up. |
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| 22:13 | | * Derakon eyes this. |
| 22:13 | <@Derakon> | Apparently rotational transformation matrices don't work like I expected them to. |
| 22:14 | <@McMartin> | Heh |
| 22:14 | <@McMartin> | To the OpenGL books~ |
| 22:14 | <@Derakon> | http://pastebin.starforge.co.uk/276 |
| 22:15 | <@Derakon> | (Note there's a line skipped between 3 and 4...oops) |
| 22:15 | <@Derakon> | (Not relevant; it just defines foo) |
| 22:15 | <@Derakon> | Anyway, I was expecting a 90? rotation about the center of the array. Clearly this isn't what I got. |
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| 22:24 | <@Derakon> | G'day, Vorn. |
| 22:27 | < gnolam> | I'm surprised it doesn't throw an error of some kind. |
| 22:27 | <@Derakon> | Why would it? |
| 22:27 | < gnolam> | Since it appears you're trying to multiply a 3x3 matrix with a 2x2 matrix. |
| 22:27 | <@Derakon> | No... |
| 22:28 | <@Derakon> | I'm calling affine_transform, which accepts an arbitrary matrix and a transformation matrix suitable to the number of dimensions of the first matrix. |
| 22:28 | <@Derakon> | http://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.interpolation. affine_transform.html |
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| --- Log closed Tue Apr 27 00:00:10 2010 |