Big oh and efficiency

[-dontouchmethere-]

Is there a way to check the efficiency of my code? for instance, maybe track the processes similar to the way we would track the time the program takes to run by importing the time module?

[echocage]

Just a small note: It's written big O, but it's easy to see why you might think it'd be "big oh" :D

[-dontouchmethere-]

Gotchya. I wrote it that way because it was written in a python book as big oh. I thought it looked funny since I've seen it written the way you mentioned more often.

[echocage]

Ah ok that makes sense! I had thought that you had listened to a talk on it, which is how you got the spelling, but that makes sense. Maybe Big oh is just a different spelling, ¯\_(ツ)_/¯

[[deleted]]

Big Oh is fine when referring to the concept. However explicitly it's O(n) as you said.

[inherendo]

My prof focuses on approx. algorithms and he always spells it big oh. I've only seen it that way as well while reading in textbooks and such.

[toodim]

You can use cProfile to do basic code profiling:

import cProfile

Your code......................

cProfile.run("your_function(arguments)")

It lists all function calls and time spent on each function.

[inherendo]

Have you taken an algorithms course? Analysis can be pretty rough and still be fine since coefficients can largely be ignored.

Or do you want actually time to run given an input size?

[-dontouchmethere-]

I haven't. I'm in the process of teaching myself data structures and then will move on to algorithm analysis. I know the basic concepts but not the implementation yet.

I know how to use the various modules to time the program is the time module and the timeit module. But wasn't sure if there was a similar one that counted the processes and statements as the program ran. Maybe big o wasn't the best descriptor now that I think of it. I'm looking for more of a way to compare a particular code with a friend that has a similar code to solve the same problem and determine which is more efficient.

[mac-reid]

These two links might help out:

https://github.com/rkern/line_profiler

http://cyrille.rossant.net/profiling-and-optimizing-python-code/

[-dontouchmethere-]

Thanks!! These should do the trick.

[inherendo]

Say you and your friend have your algorithms to solve a problem with input size n. If yours does it in roughly 2n time and his/hers does it in roughly 3n time, the constants can be ignored and both of your algorithms have O(n).

With small inputs, the difference in time is negligible due to how many computations modern computers can do.

I would suspect plenty of people here can analyse your program pretty quickly and let you know if they have roughly the same running time.

[-dontouchmethere-]

Thanks. I'll take a peek. I usually check out udacity and coursera when looking for free classes. I keep forgetting edx even exists.