# Add random numbess to matrix

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armin m on 30 Nov 2021
Commented: armin m on 2 Dec 2021
Hi. I have 1×n matrix.i wanna add 1 to 5 percent of it,s actual value to it, randomly. Can any body help.me? Tnx
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armin m on 30 Nov 2021
Q is matrix, i wanna it be normal distribute.

DGM on 30 Nov 2021
Edited: DGM on 30 Nov 2021
Define "add 1-5% of its actual value to it"
Consider an array A. Does this mean
% random factor is scalar
B = A + (0.01+0.04*rand(1))*A;
or maybe
% random factor is an array
% each element gets its own random factor
B = A + (0.01+0.04*rand(size(A))).*A;
Imnoise() allows the application of additive gaussian noise using intensity mapping of local noise variance. That might also apply.
armin m on 2 Dec 2021

Thank you very much

dpb on 1 Dec 2021
If the desire is a bounded, symmetric, continuous distribution that approximates a normal, consider the beta with, eta,gamma equal. The pdf is then bounded between [0, 1] with mean gamm/(eta+gamma) --> gamma/(2*gamma) --> 0.5 for eta==gamma.
As for the normal, you can shift and scale the generated RNVs generated from random by whatever is needed to match the target range.
The 'pdf' normalization inside histogram results in the red overlaid normal; scaling the N() pdf to match the peak bin in the histogram results in the black overlay which emphasizes the extra weight of the beta towards the central tendency as compared to a normal. But, you can produce a bounded random variate this way that with the very nebulous requirements for the underlying error distribution could surely serve the purpose.
The above was generated by
rB55=random('beta',5,5,1e6,1);
histogram(rB55)
hold on
[mn,sd]=normfit(rB55)
pN55=normpdf(x,0.5,sd);
plot(x,pN55,'-r')
plot(x,pN55*2.48/2.64,'-k')
hLg=legend('pdf(\beta(5,5))','N(0.5,sd(\beta)','0.94*N(0.5,sd(\beta)');
where the magic constants were obtained by getting the maxima of the histogram binned values and the pdf peak
The above uses functions in the Statistics Toolbox...
dpb on 1 Dec 2021
Illustrates can have very broad to quite narrow range depending on the input paramters. While not plotted, note that the B(1,1) case reduces to the uniform distribution.