优化特征规范化代码[重复]

Question

给定一个矩阵X 具有任意数量的列和行（每一行代表数据集的一个特征），我想将每个值标准化为((value - column mean) / column standard deviation)。我想出了以下代码，它有效。这可以针对更少的计算进行优化还是最优？

mu = mean(X);
sigma = std(X);
x_ones = ones(size(X));
zero_mean_X = X - x_ones * diag(mu);
X_norm = zero_mean_X ./ (x_ones * diag(sigma));

Answer 1

这是使用 Matlab 中的 bsxfun 进行的优化

M = mean(X);
S = std(X);
Y = bsxfun(@minus,X, M);
Y = bsxfun(@rdivide, Y, S);

快了4倍

X = rand(1000);

t = zeros(100,2);
for ii = 1:100
    tic;
    M = mean(X);
    S = std(X);
    Y = bsxfun(@minus,X, M);
    Y = bsxfun(@rdivide, Y, S);
    t(ii,1) = toc;
end


for ii = 1:100
    tic;
    mu = mean(X);
    sigma = std(X);
    x_ones = ones(size(X));
    zero_mean_X = X - x_ones * diag(mu);
    X_norm = zero_mean_X ./ (x_ones * diag(sigma));
    t(ii,2) = toc;
end

figure('Color', 'w');
plot(t);
legend({'bsxfun', 'matrix'});
xlabel('NUmber of simulations');
ylabel('Time');