少了，多了MATLABesque？

Question

我已经编写了一段MATLAB代码，(希望)计算CECT。我有两个心理生理学度量矩阵-一个用于大脑数据，一个用于心率；两者都是格式的历次(即10秒长间隔的多个历元)。假设的结果是用窄箱计算短窗的EEG与用较宽的垃圾箱计算较长窗的心率之间的相关性。基本的想法是，早期的大脑效应可以预测几秒钟后心率的加快。用作者的话(来自链接中的付费文件)：

为了能在350-420个试验中计算出每个人在伪影筛查后可以进行的分析，脑电图片段被分成50箱，每箱15.625 ms，从0 ms (刺激起效)到781.25 ms不等。将HP片段划分为5 0 0 ms的10个桶，范围为0~5 S，再用相关(Pearson‘s r)方法计算每个脑电箱与每一HP脑电垃圾箱的相互关系，得到10×50互相关的CECT矩阵。

我的代码与此有一点不同，因为我计算的是重叠滑动窗口，而不是不重叠的连续回收箱。而且，我的窗户也略有不同。然而，我最关心的是，我有预感我的代码是非常次优的。

目前，程序是在脑数据箱上进行I-循环，使用For循环收集心率数据向量，在心本向量和单个脑桶之间进行相关，存储产生的相关向量，然后继续到下一个向量。

重要的是，心脏和大脑数据的搜索窗口和存储窗口有不同的长度(心率变化比大脑效应慢)。此外，这最终将被计算为多个主题；我省略了数据的这个维度。

% data is a matrix of format (channels * times * epochs)
% times is a matrix of time points corresponding to sampling points

% CECT is supposed to be a matrix of correlation coefficients,
% dimensions (braintime * hearttime)

scalefac = 1000/samplingrate;

brainchannel = 12;  % channel # for brain data
heartchannel = 62;  % channel # for heart data

brainbin = 10;      % bin length for brain data (symmetric, in msec)
heartbin = 250;     % bin length for heart data (= 500 msec window)

heart = 5500;       % max point (from zero, in msec) for heart data
brain = 2500;       % max point (from zero, in msec) for brain data
first = -500;       % first point (from zero, in msec)

yy = 1;             % counter for looping over brain data
for x = find(times>first,1):find(times>brain,1)
    t0 = (x-brainbin/scalefac);
    t1 = (x+brainbin/scalefac);
    eeg = mean(squeeze(data(brainchannel,t0:t1,:)),1);
    p=1;
    clear hps
    for y = find(times>first,1):find(times>heart,1)
        t0 = (y-heartbin/scalefac);
        t1 = (y+heartbin/scalefac);

        hps(p,:) = mean(squeeze(data(heartchannel,t0:t1,:)),1);
        p = p+1;    % counter for looping over heart data
    end
    CECT(yy,:) = corr(squeeze(eeg).',hps.');
    yy = yy+1;
end

我如何使这段代码不那么糟糕(更快，更结构化)？

Answer 1

有了这些随机数据和几周时间，我的时间减少了大约30倍。内环可以外接，这是最大的区别。

clear all

n=800;
data = randn(2,n,30);
times=linspace(-1900,8600,n);

tic;

samplingrate = 200;
scalefac = 1000/samplingrate;

brainchannel = 12;  % channel # for brain data
heartchannel = 62;  % channel # for heart data

brainchannel = 1;
heartchannel = 2; %adjusted for test data

brainbin = 10;      % bin length for brain data (symmetric, in msec)
heartbin = 250;     % bin length for heart data (= 500 msec window)

heart = 5500;       % max point (from zero, in msec) for heart data
brain = 2500;       % max point (from zero, in msec) for brain data
first = -500;       % first point (from zero, in msec)


xvec=find(times>first,1):find(times>brain,1);
yvec=find(times>first,1):find(times>heart,1);

CECT=zeros(length(xvec),length(yvec));  %initialize

braindata=squeeze(data(brainchannel,(xvec(1)-heartbin/scalefac):(xvec(end)+brainbin/scalefac),:));
bn=length((xvec(1)-brainbin/scalefac):(xvec(1)+brainbin/scalefac));

heartdata=squeeze(data(heartchannel,(yvec(1)-heartbin/scalefac):(yvec(end)+heartbin/scalefac),:));
hn=length((yvec(1)-heartbin/scalefac):(yvec(1)+heartbin/scalefac));
hps=zeros(length(yvec),size(data,3));

p=1;
for y = yvec
    hps(p,:) = mean(heartdata(p:p-1+hn,:),1);
    p = p+1;    % counter for looping over heart data
end

yy = 1;             % counter for looping over brain data
for x = xvec
    eeg = mean(braindata(yy:yy-1+bn,:),1);
    CECT(yy,:) = corr(squeeze(eeg).',hps.');
    %%CECT(yy,:) = eeg * hps.'; %for testing
    yy = yy+1;
end

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