big_O
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pberkes
Python module to estimate big-O time complexity from execution time
Running [this code](https://gist.github.com/cool-RR/a5c90d9580c8a887e94cbb0e31c5ebf1) under Python 3.8 produced the following traceback. Note that it seems that `max_n=10**7` is related to this bug, because it didn't happen before I used that. ```...
Hi, Thanks for sharing this module. I am trying to run my custom code and would like to know "time complexity" of the following code: ``` def nxtGreaterQuery(arr,qur): for i...
Following the discussion in #37 , we need to substitute the current `big_O` examples in the documentation with new ones that are not so sensitive to the state of the...
The library title on Github is `big_O`, but one needs `pip install big-O` to install it. This ambiguity can be easily solved by including a short installation section to the...
Hello! Thank you for this nice package, it is very useful for performing numerical time complexity analysis. I observed that it would be nice for larger test runs to have...
Having to type the following: ```python best, others = big_o.big_o(heapify, data_generator_heapify, max_n=10**7) print(best) print() for other in others: print(other) print() ``` Is quite a drag. Also there needs to be...
How to calculate the big_o [for] feedforward based neural network class?
Complexity classes with more parameters will always fit the data better. Use cross-validation methods, or just an AIC correction, to take that into account.
- add `.github/workflows/run-test.yml` - define config in `yml` file and run tests - remove `.travis.yml`
Convert the TravisCI testing task to Github actions Also, remove the TravisCI task