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結果:
Always!
29%
It depends
14%
Never!
21%
I didn't know that was possible
36%
1810 票
Base case:
Suppose you need to do a computation many times. We are going to assume that this computation cannot be vectorized. The simplest case is to use a for loop:
number_of_elements = 1e6;
test_fcn = @(x) sqrt(x) / x;
tic
for i = 1:number_of_elements
x(i) = test_fcn(i);
end
t_forward = toc;
disp(t_forward + " seconds")
Preallocation:
This can easily be sped up by preallocating the variable that houses results:
tic
x = zeros(number_of_elements, 1);
for i = 1:number_of_elements
x(i) = test_fcn(i);
end
t_forward_prealloc = toc;
disp(t_forward_prealloc + " seconds")
In this example, preallocation speeds up the loop by a factor of about three to four (running in R2024a). Comment below if you get dramatically different results.
disp(sprintf("%.1f", t_forward / t_forward_prealloc))
Run it in reverse:
Is there a way to skip the explicit preallocation and still be fast? Indeed, there is.
clear x
tic
for i = number_of_elements:-1:1
x(i) = test_fcn(i);
end
t_backward = toc;
disp(t_backward + " seconds")
By running the loop backwards, the preallocation is implicitly performed during the first iteration and the loop runs in about the same time (within statistical noise):
disp(sprintf("%.2f", t_forward_prealloc / t_backward))
Do you get similar results when running this code? Let us know your thoughts in the comments below.
Beneficial side effect:
Have you ever had to use a for loop to delete elements from a vector? If so, keeping track of index offsets can be tricky, as deleting any element shifts all those that come after. By running the for loop in reverse, you don't need to worry about index offsets while deleting elements.
quick / easy
21%
themed / in a group
20%
challenge (e.g., banned functions)
13%
puzzle / game
16%
educational
28%
other (comment below)
3%
117 票
isstring
11%
ischar
7%
iscellstr
13%
isletter
21%
isspace
9%
ispunctuation
37%
2455 票
Don't use / What are Projects?
26%
1–10
31%
11–20
15%
21–30
9%
31–50
7%
51+ (comment below)
12%
4070 票
2
17%
3
12%
4
59%
6
4%
8
5%
Other (comment below)
3%
6419 票
numel(v)
6%
length(v)
13%
width(v)
14%
nnz(v)
8%
size(v, 1)
27%
sum(v > 0)
31%
2537 票
ismissing( { [ ] } )
26%
ismissing( NaN )
18%
ismissing( NaT )
11%
ismissing( missing )
21%
ismissing( categorical(missing) )
9%
ismissing( { '' } ) % 2 apostrophes
16%
896 票
isempty( [ ] )
10%
isempty( { } )
13%
isempty( '' ) % 2 single quotes
13%
isempty( "" ) % 2 double quotes
24%
c = categorical( [ ] ); isempty(c)
18%
s = struct("a", [ ] ); isempty(s.a)
22%
1324 票
sort(v)
8%
unique(v)
16%
union(v, [ ])
17%
intersect(v, v)
14%
setdiff(v, [ ])
12%
All return sorted output
33%
1193 票
s = ['M','A','T','L','A','B']
9%
char([77,65,84,76,65,66])
7%
"MAT" + "LAB"
21%
upper(char('matlab' - '0' + 48))
17%
fliplr("BALTAM")
17%
rot90(rot90('BALTAM'))
30%
2929 票
eye(3) - diag(ones(1,3))
11%
0 ./ ones(3)
9%
cos(repmat(pi/2, [3,3]))
16%
zeros(3)
20%
A(3, 3) = 0
32%
mtimes([1;1;0], [0,0,0])
12%
3009 票
1
33%
2
34%
3
18%
4
5%
5
3%
6+
6%
1643 票
<= 6 GB
10%
7–12 GB
26%
13–22 GB
34%
23–46 GB
19%
47–90 GB
6%
>= 91 GB
6%
15925 票
Yes, the available tools are great
12%
Yes, the available tools need help
6%
No, but I would like to
14%
No, it is not important to me
7%
What is test-driven development?
61%
1955 票
Always
12%
Sometimes
11%
In the past, but not now
3%
Never
20%
What is Simulink Project?
53%
2443 票
Always
8%
Sometimes
9%
In the past, but not now
2%
Never
23%
What is MATLAB Project?
58%
4533 票
A bunch of quick, simple problems
16%
A structured, educational set
15%
Brain-teasers
16%
Random miscellany
3%
Something else
2%
What are Problem Groups?
49%
1777 票
Cody has a wealth of problem groups that allow users of various skill levels to improve programming skills vis-à-vis MATLAB in an engaging way.
I would like to highlight the Draw Letters group, composed of problems created by Majid Farzaneh.
If you haven't yet visited Cody or solved that problem group, I would recommend that you head over there now. What are you waiting for?