首先你要删除你的 for 循环,使其成为向量乘法:
tic
a = zeros(1,50000);
b = [1:50000];
a = 10.*b-5;
result = sum(a);
toc
Elapsed time is 0.008504 seconds.
另一种方法是简化您的操作,您将 1 到 50000 乘以 10 并减去 5 然后取总和(这是一个数字),相当于:
tic
result = sum(1:50000)*10 - 5*50000;
toc
Elapsed time is 0.003851 seconds.
或者如果你真的很喜欢数学(这是一种纯粹的数学表达方法):
tic
result = (1+50000)*(50000/2)*10 - 5*50000;
toc
Elapsed time is 0.003702 seconds.
如您所见,一点点数学运算可以比纯粹的高效编程做得更好,实际上,循环并不总是很慢,在您的情况下,循环实际上比矢量化方法更快:
tic
a = zeros(1,50000);
for n = 1:50000
a(n)=10.*n-5;
end
sum(a);
toc
Elapsed time is 0.006431 seconds.
时间
让我们做一些时间,看看结果。底部提供了自己运行的功能。 execTime 的大致执行时间以秒为单位,impPercentage 的改进百分比以 % 为单位。
结果
代码
以下函数可用于生成输出。请注意,它至少需要 R2013b 才能使用内置的 timeit-function 和 table。
function timings
%feature('accel','on') %// commented out because it's undocumented
cycleCount = 100;
execTime = zeros(4,cycleCount);
names = {'loop';'vectorized';'adiel';'math'};
w = warning;
warning('off','MATLAB:timeit:HighOverhead');
for k = 1:cycleCount
execTime(1,k) = timeit(@()loop,1);
execTime(2,k) = timeit(@()vectorized,1);
execTime(3,k) = timeit(@()adiel,1);
execTime(4,k) = timeit(@()math,1);
end
warning(w);
execTime = min(execTime,[],2);
impPercentage = (1 - execTime/max(execTime)) * 100;
table(execTime,impPercentage,'RowNames',names)
function result = loop
a = zeros(1,50000);
for n = 1:50000
a(n) = 10.*n - 5;
end
result = sum(a);
function result = vectorized
b = 1:50000;
a = 10.*b - 5;
result = sum(a);
function result = adiel
result = sum(1:50000)*10 - 5*50000;
function result = math
result = (1+50000)*(50000/2)*10 - 5*50000;