Using bootstrp with my own defined function

Hello

I am currently tasked with fitting 2 sets of data. I extracted 5 datapoints as a training set and I created a function that works the percentage differnce between the two sets which is as follows;

% img is a 1D normlised matrix and adam is a count of a certain feature in the images. Each image is stored in a cell array
function erfun = per_dif(img, adam, thresh, siz)

tota = 0;
totb = 0;

for i = 1:5
    err = abs((round(sum(sum(img{i}.*img{i}>thresh == 1)))/siz)-adam(i));
    tota = tota + err;
    totb = totb + adam(i);
end
erfun = tota/totb;
end

In reality, the 'thresh' & 'siz' variables are unknown, I set a function as follows;

    fun = @(x)per_dif(adam_im,adam_count,x(1),x(2))

and I used the fminsearch function on it as follows

x0 = [0.5,1];
x = fminsearch(f,x0)

I used the values of x on the test set and I got a result that makes sense.

Now, I want to increase the accuracy of my model using the bootstrp method. Instead of setting aside 5 training and 5 test data points, I want to use my whole set, and iterate through a random 5 of them to get multiple values of x (and then use mean and standard deviation).

It isjust that I cant seem to get the code working. This is what i have right now;

f = @(x)fminsearch(per_dif(adam_im, adam_count,x(1),x(2)),x0);
bootstat = bootstrp(200,@f,[adam_im,adam_count])

Answer 1

[udi ibgui]

I have recreated a function that minimises my data

function x = per_dif_fit(img, adam)

x0 = [0.5,1];
tota = @(x)0;
totb = 0;

for i = 1:5
    err = @(x)abs((round(sum(sum(img{i}.*img{i}>x(1) == 1)))/x(2))-adam(i));
    tota = @(x)tota(x) + err(x);
    totb = totb + adam(i);
end

a = @(x)tota(x)/totb;

x = fminsearch(a,x0)
end

and used that as an input to the bootstrp. It worked