これを試して:
Difference = zeros(NObjects,NClusters);
costsTmp = zeros(NObjects,NClusters);
lambda = zeros(NObjects,NClusters);
for clustclust = 1:NClusters
repeated_curCenter = repmat(curCenter(clustclust,:), NObjects, 1);
% ^^ This creates a repeated matrix of 1 cluster center but with NObject
% rows. Now, dimensions of repeated_curCenter equals that of curPartData
Difference(:,clustclust) = repeated_curCenter - curPartData;
costsTmp(:,clustclust) = sqrt(sum(abs(costsTmp(:,clustclust)).^2, 1)); %Euclidean norm
end
アプローチは、等しい次元の行列を作成しようとすることです。次のように 2 つの 3D 配列を作成してこの概念を拡張することにより、現在の for ループを排除することもできます。
costTmp = zeros(NObjects,NClusters); lambda = zeros(NObjects,NClusters);
%Assume that number of dimensions for data = n
%curCenter's dimensions = NClusters x n
repeated_curCenter = repmat(curCenter, 1, 1, NObjects);
%repeated_curCenter's dimensions = NClusters x n x NObjects
%curPartData's dimensions = NObject x n
repeated_curPartData = repmat(curPartData, 1, 1, NClusters);
%repeated_curPartData's dimensions = NObjects x n x NClusters
%Alligning the matrices along similar dimensions. After this, both matrices
%have dimensions of NObjects x n x NClusters
new_repeated_curCenter = permute(repeated_curCenter, [3, 2, 1]);
Difference = new_repeated_curCenter - repeated_curPartData;
Norm = sqrt(sum(abs(Difference)).^2, 2); %sums along the 2nd dimensions i.e. n
%Norm's dimensions are now NObjects x 1 x NClusters.
Norm = permute(Norm, [1, 3, 2]);
ここで、Norm は costTmp のようなものですが、追加の次元があります。ラムダのコードを提供していません。質問のコードにもラムダが何であるかわかりません。