X_poly_test and normalize (using mu and sigma) X_poly_test = polyFeatures(Xtest, p); X_poly_test = bsxfun (@minus, X_poly_test, mu); X_poly_test = bsxfun(@rdivide, X_poly_test, sigma); X_poly_test = [ones(size Map X_poly_val and normalize (using mu and sigma) X_poly_val = polyFeatures(Xval, p); X_poly_val = bsxfun (@minus, X_poly_val, mu); X_poly_val = bsxfun(@rdivide, X_poly_val, sigma); X_poly_val = [ones(size(X_poly_val
mu = mean(X); X_norm = bsxfun(@minus, X, mu); sigma = std(X_norm); X_norm = bsxfun(@rdivide, X_norm,
train_targets'); X = train_digitdata; Y = train_targets; %输入数据初始化 Xmin = min(X); Xmax = max(X); X = bsxfun (@rdivide,bsxfun(@minus,X,Xmin),(Xmax-Xmin)); %RBM训练得到第一隐层的网络参数,rbm输入为图片数据 rbm1 = rbm([784,400]); rbm1
% 模糊图的时延 fd = linspace(-150,150,Np); % 模糊图的频率 ambi = abs(xcorr2(bsxfun
. % mu = mean(X)'; sigma2 = (sum(bsxfun(@minus,X,mu').^2).
MATLAB 核心代码 tl = linspace(0,1,Np); % 模糊图的时延 fd = linspace(-150,150,Np); % 模糊图的频率 ambi = abs(xcorr2(bsxfun
% calculate the translation filter update 预测的滤波器更新,这 xlf = fft2(xl); new_hf_num = bsxfun % calculate the scale filter update xsf = fft(xs,[],2); %没一行做fft new_sf_num = bsxfun
{in}(:,ik) = reshape(cnn.a{in-1}(:,:,ik,:),[height*width*kernel_num,1]); end case 'full' cnn.z{in}= bsxfun cnn.weights{in}*cnn.a{in-1},cnn.biases{in}); cnn.a{in} = sigmoid(cnn.z{in}); case 'output' cnn.z{in}= bsxfun
27、常用向量化函数 —— accumarray、arrayfun 、bsxfun 、cellfun 、spfun 、structfun等。
% LINECOLORS N=6; X =linspace(0,pi*3,1000); Y =bsxfun(@(x,n)sin(x+2*n*pi/N), X.
27、常用向量化函数 —— accumarray、arrayfun 、bsxfun 、cellfun 、spfun 、structfun等。
+1)=3; end end end dataMean=mean(imdb.images.data,4); imdb.images.data = single(bsxfun
bsxfun 强大的、万能的、不同维数的矩阵扩展混合运算,从此告别矩阵运算中的for循环 另,matlab里所有以fun为后缀的命令都很好用,arrayfun,cellfun,structfun,等等