How to store all outputs from this nested for loop

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Leo Tu
Leo Tu 2021 年 7 月 9 日
コメント済み: Leo Tu 2021 年 7 月 9 日
I have this for loop which goes through 3 different variables. I need to record all outputs so that I can plot the 3 variables and the outputs as a 3D colour surface plot similar to the second plot here https://www.mathworks.com/help/matlab/ref/surf.html
At the moment it only gives the last output so if someone can point me in the right direction to gettiing all outputs.
for s=0:9 % Variable 1
for om=0.1:0.1:1 % Variable 2
for mu=0.1:0.1:1 % Variable 3
nll = mylikelihood(s,om,mu); % This is a function I made.
end
end
end
Also if anyone could see how I can vectorise the code so that it runs faster that would be appreciated.
  2 件のコメント
Leo Tu
Leo Tu 2021 年 7 月 9 日
Thank you @Walter Roberson, the function is quite long as it contains a function itself and requires the temperatures vector attached. I'm not sure if it can be vectorized any further.
function nll = mylikelihood(s,om,mu)
% output; nll is the negative log likelihood
% input; s is lag, om is decay rate, mu is baseline rate
% T is temperatures, t = time in days
load("Temperature.mat")
T = T'; % from column vector to row vector
AT = T-min(T); % this is the adjusted positive temperatures
r = mypoiss(5,0.5,1); % this function can be seen below
t = max(s)+1:numel(AT);
for ti = 1:numel(t)
k = abs(s:-1:(s-t(ti)+1));
id = ((t(ti)-1):-1:0)+1;
lambda(t(ti)) = mu+sum(om.^k.*AT(id))/sum(om.^k);
% store all the results
end
nll = 0;
nll = nll - (r(ti).*log(lambda(t(ti)))) + (lambda(t(ti)));
end
function r = mypoiss(s,om,mu)
% output; r is synthetic time series for number of cases
% input; s is lag, om is decay rate, mu is baseline rate
% T is temperatures, t = time in days
load("Temperature.mat")
T = T'; % from column vector to row vector
AT = T-min(T); % this is the adjusted positive temperatures
for t=s+1:length(AT)
k = abs(s:-1:(s-t+1));
id=[(t-1):-1:0]+1;
lambda(t) = mu+sum(om.^k.*AT(id))/sum(om.^k);
end
r = poissrnd(lambda);

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Stephen
Stephen 2021 年 7 月 9 日
Vs = 0:9; % Variable 1
Vom = 0.1:0.1:1; % Variable 2
Vmu = 0.1:0.1:1; % Variable 3
Ns = numel(Vs);
Nom = numel(Vom);
Nmu = numel(Vmu);
nll = nan(Ns,Nom,Nmu);
for k1 = 1:Ns
for k2 = 1:Nom
for k3 = 1:Nmu
nll(k1,k2,k3) = mylikelihood(Vs(k1),Vom(k2),Vmu(k3));
end
end
end
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Leo Tu
Leo Tu 2021 年 7 月 9 日
@Stephen Cobeldick Thank you for your help!

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