in not working for arrays is silly, change my view

Steven Schveighoffer schveiguy at
Mon Mar 2 21:33:37 UTC 2020

On 3/2/20 3:52 PM, aliak wrote:
> On Monday, 2 March 2020 at 15:47:26 UTC, Steven Schveighoffer wrote:
>> On 3/2/20 6:52 AM, Andrea Fontana wrote:
>>> On Saturday, 29 February 2020 at 20:11:24 UTC, Steven Schveighoffer 
>>> wrote:
>>>> 1. in is supposed to be O(lg(n)) or better. Generic code may depend 
>>>> on this property. Searching an array is O(n).
>>> Probably it should work if we're using a "SortedRange".
>>> int[] a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9];
>>> auto p = assumeSorted(a);
>>> assert(3 in p);
>> That could work. Currently, you need to use p.contains(3). opIn could 
>> be added as a shortcut.
>> It only makes sense if you have it as a literal though, as 
>> p.contains(3) isn't that bad to use:
>> assert(3 in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9].assumeSorted);
> There's no guarantee that checking if a value is in a sorted list is any 
> faster than checking if it's in a non sorted list. It's why sort usually 
> switches from a binary-esque algorithm to a linear one at a certain 
> size.

Well of course! A binary search needs Lg(n) comparisons for pretty much 
any value, whereas a linear search is going to end early when it finds 
it. So there's no guarantee that searching for an element in the list is 
going to be faster one way or the other. But Binary search is going to 
be faster overall because the complexity is favorable.

> The list could potentially need to be _very_ large for p.contains 
> to make a significant impact over canFind(p) AFAIK.
> Here's a small test program, try playing with the numbers and see what 
> happens:
> import std.random;
> import std.range;
> import std.algorithm;
> import std.datetime.stopwatch;
> import std.stdio;
> void main()
> {
>      auto count = 1_000;
>      auto max = int.max;
>      alias randoms = generate!(() => uniform(0, max));
>      auto r1 = randoms.take(count).array;
>      auto r2 = r1.dup.sort;
>      auto elem = r1[uniform(0, count)];

auto elem = r1[$-1]; // try this instead

>      benchmark!(
>          () => r1.canFind(elem),
>          () => r2.contains(elem),
>      )(1_000).writeln;
> }
> Use LDC and -O3 of course. I was hard pressed to get the sorted contains 
> to be any faster than canFind.
> This begs the question then: do these requirements on in make any sense? 
> An algorithm can be log n (ala the sorted search) but still be a 
> magnitude slower than a linear search... what has the world come to 🤦‍♂️
> PS: Why is it named contains if it's on a SortedRange and canFind 
> otherwise?

A SortedRange uses O(lgn) steps vs. canFind which uses O(n) steps.

If you change your code to testing 1000 random numbers, instead of a 
random number guaranteed to be included, then you will see a significant 
improvement with the sorted version. I found it to be about 10x faster. 
(most of the time, none of the other random numbers are included). Even 
if you randomly select 1000 numbers from the elements, the binary search 
will be faster. In my tests, it was about 5x faster.

Note that the compiler can do a lot more tricks for linear searches, and 
CPUs are REALLY good at searching sequential data. But complexity is 
still going to win out eventually over heuristics. Phobos needs to be a 
general library, not one that only caters to certain situations.


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