如何在Matlab中的多维数组中应用corr2函数？

假设我有两个矩阵 A 和 B

A = rand(4,5,3);
B = rand(4,5,6)

我想应用函数“corr2”来计算相关系数。

corr2(A(:,:,1),B(:,:,1))
corr2(A(:,:,1),B(:,:,2))
corr2(A(:,:,1),B(:,:,3))
...
corr2(A(:,:,1),B(:,:,6))
...
corr2(A(:,:,2),B(:,:,1))
corr2(A(:,:,2),B(:,:,2))
...
corr2(A(:,:,3),B(:,:,6))

如何避免使用循环来创建此类矢量化？

侵入 m 文件corr2创建用于处理 3D 阵列的自定义矢量化版本。这里提出了两种方法bsxfun（当然！）

方法#1

szA = size(A);
szB = size(B);

a1 = bsxfun(@minus,A,mean(mean(A)));
b1 = bsxfun(@minus,B,mean(mean(B)));

sa1 = sum(sum(a1.*a1));
sb1 = sum(sum(b1.*b1));

v1 = reshape(b1,[],szB(3)).'*reshape(a1,[],szA(3));
v2 = sqrt(sb1(:)*sa1(:).');

corr3_out = v1./v2; %// desired output

corr3_out stores corr2所有 3D 切片之间的结果A and B.

因此，对于A = rand(4,5,3), B = rand(4,5,6)， 我们会有corr3_out as a 6x3 array.

方法#2

稍微不同的方法可以节省很少的调用sum and mean通过使用reshape反而 -

szA = size(A);
szB = size(B);
dim12 = szA(1)*szA(2);

a1 = bsxfun(@minus,A,mean(reshape(A,dim12,1,[])));
b1 = bsxfun(@minus,B,mean(reshape(B,dim12,1,[])));

v1 = reshape(b1,[],szB(3)).'*reshape(a1,[],szA(3));
v2 = sqrt(sum(reshape(b1.*b1,dim12,[])).'*sum(reshape(a1.*a1,dim12,[])));

corr3_out = v1./v2; %// desired output

标杆管理

基准代码 -

%// Create random input arrays
N = 55; %// datasize scaling factor
A = rand(4*N,5*N,3*N);
B = rand(4*N,5*N,6*N);

%// Warm up tic/toc
for k = 1:50000
    tic(); elapsed = toc(); 
end

%// Run vectorized and loopy approach codes on the input arrays

%// 1. Vectorized approach
%//... solution code (Approach #2) posted earlier
%// clear variables used

%// 2. Loopy approach
tic
s_A=size(A,3);
s_B=size(B,3);
out1 = zeros(s_B,s_A);
for ii=1:s_A
    for jj=1:s_B
        out1(jj,ii)=corr2(A(:,:,ii),B(:,:,jj));
    end
end
toc

结果 -

-------------------------- With BSXFUN vectorized solution 
Elapsed time is 1.231230 seconds.
-------------------------- With loopy approach
Elapsed time is 139.934719 seconds.

MATLAB-JIT 爱好者在这里表现出一些爱！ :)