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HMasterSunday t1_iw9allv wrote

Reply to comment by sckuzzle in Making a model predict on the basis of a particular value by ole72444

also: numpy.split can create several cuts off of a numpy array so it simplifies to:

import numpy as np def process_data(input_array): cut_array = numpy.split(input_array, (len(array)/4)) max_array =[ ] for cut in cut_array: max_array.append(max(cut)) return max_array

much shorter, used this method recently so it's on the front of my mind

edit: don't know how to format on here, sorry

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sckuzzle t1_iw9fe1i wrote

Writing "short" code isn't a always good thing. Yes your suggestion has less lines, but:

  • It takes ~6 times as long to run

  • It does not return the correct output (split does not take every nth value, but rather groups it into n groups)

I'm absolutely not claiming my code was optimized, but it did clearly show the steps required to calculate the necessary output, so it was easy to understand. Writing "short" code is much more difficult to understand what is happening, and often leads to a bug (as seen here). Also, depending on how you are doing it, it often takes longer to run (the way it was implemented requires it to do extra steps which aren't necessary).

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HMasterSunday t1_iw9qr8l wrote

Interesting, I didn't try a test run to time both approaches, I'll do that more often. As per your other point though, my code does account for that already, the number of individual cuts is 1/4 of the length of the full array (len(input_array)/4) so it splits it up into arrays of length 4 anyways. That much I do know at least.

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sckuzzle t1_iwaia67 wrote

> As per your other point though, my code does account for that already

You may try running it? It returns [3.0, 8.0, 12.0, 8.0]. The intended output is [False, False, True, False]. OP didn't ask for it to be split into groups of four, they asked for every fourth value to be taken.

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