Assuming that: You can compute all available discounts based on your basket Each product can only have a single discount applied to it Each discount can only be used once Then the problem becomes one that is called an assignment problem and can be optimally solved in O(n^3) using the Hungarian algorithm. You will need … Read more
Check out this paper (PDF link) by Richard Fateman. The code samples are in Lisp, in but in any event, much of the secret boils down to minimizing the number of bignum (arbitrary precision integer) calculations you have to do. Naturally, if you don’t need/have bignums, it’s trivial; either a lookup table or a simple … Read more
Updated based on feedback from @BenAston & @trincot Roughly, this is what’s happening in both cases: For loop Set the index variable to its initial value Check whether or not to exit the loop Run the body of your loop Increment the index variable Back to step 2 The only overhead that happens on every … Read more
This is the margin of error for the score. In most cases, that is a half of confidence interval. Think about it as if there is a “±” sign between “Score” and “Score error”. In fact, the human-readable log will show that: Result: 1.986 ±(99.9%) 0.009 ops/ns [Average] Statistics: (min, avg, max) = (1.984, 1.986, … Read more
Many interfaces to the linux kernel are not well documented. Or even if they seem well documented, they can be pretty complex and that can make it hard to understanding what the functional or, often even harder, nonfunctional properties of the interface are. For this reason, my advice to anyone wanting a solid understanding of … Read more
With git 2.8 (Q12016), you will be able to initiate the fetch of submodules… in parallel! See commit fbf7164 (16 Dec 2015) by Jonathan Nieder (artagnon). See commit 62104ba, commit fe85ee6, commit c553c72, commit bfb6b53, commit b4e04fb, commit 1079c4b (16 Dec 2015) by Stefan Beller (stefanbeller). (Merged by Junio C Hamano — gitster — in … Read more
Numpy is optimised for large amounts of data. Give it a tiny 3 length array and, unsurprisingly, it performs poorly. Consider a separate test import timeit reps = 100 pythonTest = timeit.Timer(‘a = [0.] * 1000000’) numpyTest = timeit.Timer(‘a = numpy.zeros(1000000)’, setup=’import numpy’) uninitialised = timeit.Timer(‘a = numpy.empty(1000000)’, setup=’import numpy’) # empty simply allocates the … Read more
DBCC FREEPROCCACHE DBCC DROPCLEANBUFFERS
In the new performance tab markers show up in “User Timing”. You need to use performance.measure() to mark the start and end of your marks like this: performance.mark(‘myMark-start’); // your code performance.mark(‘myMark-end’); performance.measure(‘myPerfMarker’, ‘myMark-start’, ‘myMark-end’);