我正在尝试优化这段代码并摆脱实现的嵌套循环。我发现将矩阵应用于 pdist 函数时遇到困难
例如, 1+j//-1+j//-1+j//-1-j 是初始点,我试图通过最小距离方法检测 0.5+0.7j 到它所属的点。
任何帮助表示赞赏
function result = minDisDetector( newPoints, InitialPoints)
result = [];
for i=1:length(newPoints)
minDistance = Inf;
for j=1:length(InitialPoints)
X = [real(newPoints(i)) imag(newPoints(i));real(InitialPoints(j)) imag(InitialPoints(j))];
d = pdist(X,'euclidean');
if d < minDistance
minDistance = d;
index = j;
end
end
result = [result; InitialPoints(index)];
end
end
最佳答案
您可以使用 Speed-efficient classification in Matlab
中列出的高效欧几里德距离计算来计算 vectorized solution
-
%// Setup the input vectors of real and imaginary into Mx2 & Nx2 arrays
A = [real(InitialPoints) imag(InitialPoints)];
Bt = [real(newPoints).' ; imag(newPoints).'];
%// Calculate squared euclidean distances. This is one of the vectorized
%// variations of performing efficient euclidean distance calculation using
%// matrix multiplication linked earlier in this post.
dists = [A.^2 ones(size(A)) -2*A ]*[ones(size(Bt)) ; Bt.^2 ; Bt];
%// Find min index for each Bt & extract corresponding elements from InitialPoints
[~,min_idx] = min(dists,[],1);
result_vectorized = InitialPoints(min_idx);
使用
newPoints
作为 400 x 1
& InitialPoints
作为 1000 x 1
的快速运行时测试:-------------------- With Original Approach
Elapsed time is 1.299187 seconds.
-------------------- With Proposed Approach
Elapsed time is 0.000263 seconds.
关于matlab - MATLAB 中复杂向量的高效分类,我们在Stack Overflow上找到一个类似的问题:https://stackoverflow.com/questions/29860637/