# How to code to perform the following task?

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Assen Beshr 2022 年 6 月 25 日

Num_DG=2;nPop=50;
Location=[2;3;4;5;6;7;8;9;10;11;12;13;14;15;16;17;18;19;20;21;22;23;24;25;26;27;28;30;31;44;45;46;48;49;77;79;80;82;86;87;88;115;116;117;118;119;120;121;122;123;124;125;126;127;128;129;130;131;132;133;134;135;136;137];
Locations = Location(randi(numel(Location),nPop,Num_DG));
s=[9476,4739,3157,2650];
Sizes=reshape(randperm(s(Num_DG),Num_DG*nPop)-1,nPop,Num_DG);
preposition=[Sizes,Locations];
% OR
min=[0 0 1 1];
max=[4738 4738 137 137];
Num_DG=2;
nVar=2*Num_DG;
VarSize=[1 nVar];
Position=unifrnd(min,max,VarSize);
when the above two task is change by two varaible with complex number like
s=[9476+23i,4739+35i,3157+54i,2650+23i];
max=[4738+34i 4738+342i 137 137]; how to modify the code to perform the same tasks?
modfiy
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dpb 2022 年 6 月 26 日
So, as noted above, a complex PRV is simply
rngReal=[0 4738];
rngImag=[0 342];
c=complex(randi(rngReal),randi(rngImag));
If you have multiple ranges and numbers of rng's to generate for each, you'll want to package the above in some caller-friendly function for ease of coding, but the technique is as shown.
There is no builtin RNG for complex variables to do a single substitution of one function for another; but, you'll have one when you've finished the above.
Of course, you've got to decide the underlying distribution of the real and imaginary parts to decide which generator to use; randi above will return only integer-valued values; rand will generate full-precision doubles but you'll have to scale to the desired range.
Torsten 2022 年 6 月 26 日

Then do as "dpb" suggests.
rng("default")
lb = [254+112i,423+312i];
ub = [588+256i,745+1256i];
lb_real = real(lb(:));
ub_real = real(ub(:));
lb_imag = imag(lb(:));
ub_imag = imag(ub(:));
rand_real = lb_real + rand(size(lb_real)).*(ub_real-lb_real);
rand_imag = lb_imag + rand(size(lb_imag)).*(ub_imag-lb_imag);
random_numbers = (rand_real + 1i*rand_imag).'
random_numbers =
1.0e+03 * 0.5261 + 0.1303i 0.7147 + 1.1742i

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### 採用された回答

dpb 2022 年 6 月 26 日

"...randi ... will return only integer-valued values; rand will generate full-precision doubles but you'll have to scale to the desired range."
The min/max ranges given were integral as were the original s PRNVs generated by the permute operation on the original s.
The first code snippet generates a vector of length Num_DG*nPop elements from the integers in the range 1:s(Num_DG).
The snippet defines Num_DG and nPop as constants; and so the permutation operation uses a specific reference to the s data as the number of possible elements to use in generating the subsequent V vector.
As noted in first comments, there is no corollary operation for this directly with complex variables; the OP will have to provide additional information on what V is used for to make some valid determination of what it means, if anything, when s is turned into a complex variable instead.
One presumes this is probably some Monte Carlo-like simulation and this was a part of a way to generate a set of RNVs for the subsequent calculation but we simply do not know enough to be able to provide a direct answer to the underlying problem.
My first suggestion of using fix(abs(s)) in lieu of s would let the code run; whether it has any use or not is another question entirely.
A more thorough understanding of the code would be required to know; it may well be that turning these values at this point in the code into complex isn't a proper thing to do at all -- but that the complex variables might come into play down in the bowels of the simulation.
If I had to really make a guess, that would be it -- the Q? asked isn't the one that should have been at all.

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