问题
I have 2 matrices = X in R^(n*m)
and W in R^(k*m)
where k<<n
.
Let x_i
be the i-th row of X and w_j
be the j-th row of W.
I need to find, for each x_i what is the j that maximizes <w_j,x_i>
I can't see a way around iterating over all the rows in X, but it there a way to find the maximum dot product without iterating every time over all of W?
A naive implementation would be:
n = 100;
m = 50;
k = 10;
X = rand(n,m);
W = rand(k,m);
Y = zeros(n, 1);
for i = 1 : n
max_ind = 1;
max_val = dot(W(1,:), X(i,:));
for j = 2 : k
cur_val = dot(W(j,:),X(i,:));
if cur_val > max_val
max_val = cur_val;
max_ind = j;
end
end
Y(i,:) = max_ind;
end
回答1:
Dot product is essentially matrix multiplication:
[~, Y] = max(W*X');
回答2:
bsxfun based approach to speed-up things for you -
[~,Y] = max(sum(bsxfun(@times,X,permute(W,[3 2 1])),2),[],3)
On my system, using your dataset I am getting a 100x+
speedup with this.
One can think of two more "closeby" approaches, but they don't seem to give any huge improvement over the earlier one -
[~,Y] = max(squeeze(sum(bsxfun(@times,X,permute(W,[3 2 1])),2)),[],2)
and
[~,Y] = max(squeeze(sum(bsxfun(@times,X',permute(W,[2 3 1]))))')
来源:https://stackoverflow.com/questions/24570982/matlab-argmax-and-dot-product-for-each-row-in-a-matrix