Speeding up the nested for loops

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Zahra Yousefi Darani 2022-11-4

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https://ww2.mathworks.cn/matlabcentral/answers/1843463-speeding-up-the-nested-for-loops

评论： Zahra Yousefi Darani 2022-11-8

Hi,

I have a nested for loop that inside the innermost loop, the result of each iteration should be saved in a 4D matrix.

Althought the calculation is more accurate than curve fitting but the speed is awful. How can I speed up this piece of code? Parloop fails due to the way I save the data.

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Zahra Yousefi Darani 2022-11-8

编辑：Bruno Luong 2022-11-8

在 MATLAB Online 中打开

Thanks a lot. Here it is the code:

close all, clear,clc
%%
tic
SampN = 1000
Params = zeros(SampN,4);
subjects = [7,8,10,11];
for SUBJ = 1 : 4
    SubjectNumber = subjects(SUBJ)
    dataname = sprintf('FB2ZY%d',SubjectNumber)
    data = cell2mat(struct2cell(load(dataname)))
    stims = data(:,3);
    stim = unique(stims)
    stimbound = stim([5,6]);
    data(:,7) = data(:,3);
    for bb = 1 : numel(stimbound)
        filter = data(:,3)==stimbound(bb);
        data(filter,7) = unique(data(:,2));
    end
    clear stims
    clear stim
    stims = data(:,7);
    stim = unique(stims);
    distance = mean(diff(stim))
    for sb = 1 : numel(stims)
        data(sb,8) = round((data(sb,7)-data(sb,2))/distance);
    end
    choices = data(:,6);
    clear stims
    clear stim
    stims = data(:,8);
    stim = unique(stims);
    IDs = stim;
    meanChoice = nan(numel(IDs),1);  % calculation of y data 
    numChoice = nan(numel(IDs),1); %size of y data 
    for iID = 1:numel(IDs)
        meanChoice(iID) = mean(choices(stims==IDs(iID)));
        numChoice(iID) = sum(stims==IDs(iID));
    end
    %%  From here I want to optimize the code, above part runs in a few portion of second.
    MaxSigma = (numel(stim)-1)*distance;
    Maxlapse = 0.1;
    SigmaRange = 0 : 0.1 : 3;
    LapseRange = 0 : 0.01 : Maxlapse;
    MinMu = median(stim)-1;
    MaxMu = median(stim)+1;
    Murange = MinMu : 0.1 : MaxMu;
    %%
    DistChoiceAllParams = zeros(numel(LapseRange),numel(LapseRange),numel(SigmaRange),numel(Murange));
    calchoice = zeros(SampN,750);
    choice = choices;
    SessID = find(data(:,1) == 1);
    for N = 1 : SampN
        SampleID = randsample([1:numel(SessID)],numel(SessID),true);
        SampleStim = data(SessID(SampleID(1)):SessID(SampleID(1))+249,8);
        SampleChoice = data(SessID(SampleID(1)):SessID(SampleID(1))+249,6);
        for ss = 2 : numel(SampleID)
            SampleStim = vertcat(SampleStim,data(SessID(SampleID(ss)):SessID(SampleID(ss))+249,8));
            SampleChoice = vertcat(SampleChoice,data(SessID(SampleID(ss)):SessID(SampleID(ss))+249,6));
        end
        NeededStim(N,:) = SampleStim;
        NeededChoice(N,:) = SampleChoice;
    end
    for N = 1 : SampN
        S = NeededStim(N,:);
        C = NeededChoice(N,:);
        for m = 1 : numel(Murange)
            mu = Murange(m);
            for sig = 1 : numel(SigmaRange)
                sigma = SigmaRange(sig);
                for la = 1 : numel(LapseRange)
                    lambda = LapseRange(la);
                    for ga = 1 : numel(LapseRange)
                        gamma = LapseRange(ga);
                        calchoice(N,:) = CalChoice(gamma,lambda,S,mu,sigma);
                        DistChoiceAllParams(ga,la,sig,m) = CrossEntr(C,calchoice(N,:));
                        
                    end
                end
            end
        end
        [MIN,idx] = min(DistChoiceAllParams,[],'all');
        [I1,I2,I3,I4] = ind2sub(size(DistChoiceAllParams),idx);
        Params(N,:) = [Murange(I4),SigmaRange(I3),LapseRange(I2),LapseRange(I1)];
    end
    name = strcat('Params_',dataname);
    feval('assignin','base',name,Params)
    save(name,name)
end

Jan 2022-11-8

编辑：Jan 2022-11-8

在 MATLAB Online 中打开

We cannot run this code due to the missing input files. Then it is very hard to improve code, which is written as a large monolithic block, because we have to guess, where the bottleneck is.

Split the code into functions. Use a function as main part also instead of using the brute clearing header close all, clear,clc. Avoid clear of variabels, because this is usually (but not in all cases) a waste of time in Matlab.

Avoid the izterative growing of arrays, because this is very expensive:

NeededStim = zeros(SampN, ???);   % Pre-allocate accordingly!!!
NeededChoice = zeros(SampN, ???); % Pre-allocate accordingly!!!
for N = 1 : SampN
    SampleID = randsample([1:numel(SessID)],numel(SessID),true);
    SampleStim = zeros(numel(SampleID), 250);
    SampleChoice = zeros(numel(SampleID), 250);
    for ss = 1 : numel(SampleID)
        SampleStim(:, ss) = data(SessID(SampleID(ss)):SessID(SampleID(ss))+249,8);
        SampleChoice(:, ss) = data(SessID(SampleID(ss)):SessID(SampleID(ss))+249,6);
    end
    NeededStim(N,:) = SampleStim(:);
    NeededChoice(N,:) = SampleChoice(:);
end

This is ugly:

feval('assignin','base',name,Params)

Because this is a script, you create a variable dynamically in the local workspace. As eval this impedes Matlab's JIT acceleration drastically: Afterwards Matlab has to check the look-up table of variables instead of using efficient pointers to the values. This can slow down loops by a factor of 100. "Optimizing" this code is not the point inthis case, but cleaing it from evil pitfalls.

Zahra Yousefi Darani 2022-11-8

Oh! Thanks for very constructive comments. I am going to rewrite the cod.

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Speeding up the nested for loops

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