“bayesopt” treats past observations as immutable. It is not possible to modify them while running “bayesopt”.
However, to access and modify past observation variables you can consider Custom Bayesian Optimization Loop.
- Use MATLAB's Gaussian Process functions (“fitrgp”, etc.) to model the objective function and predict to determine the next point to evaluate.
- In each iteration, you can manually modify any past observations before updating the model.
Refer to the below reference custom code for Bayesian Optimization loop:
objectiveFcn = @(x) (x - 2).^2 + sin(5 * x);
maxIterations = 5;
numInitialPoints = 5;
bounds = [0, 5];
u_history = linspace(bounds(1), bounds(2), numInitialPoints)';
y_history = objectiveFcn(u_history);
% Bayesian optimization loop
for n = 1:maxIterations
gpModel = fitrgp(u_history, y_history, 'KernelFunction', 'matern52', 'Standardize', true);
u_next = determineNextInput(gpModel, bounds);
y_next = objectiveFcn(u_next);
% Modify previous observation if needed
if n > 1
y_history(end) = modifyObservation(y_history(end));
end
u_history = [u_history; u_next];
y_history = [y_history; y_next];
fprintf('Iteration %d: u = %.4f, y = %.4f\n', n, u_next, y_next);
end
% determine next input using Expected Improvement
function u_next = determineNextInput(gpModel, bounds)
acquisitionFcn = @(x) -expectedImprovement(x, gpModel);
options = optimoptions('fmincon', 'Display', 'off');
u_next = fmincon(acquisitionFcn, mean(bounds), [], [], [], [], bounds(1), bounds(2), [], options);
end
% Expected Improvement acquisition function
function ei = expectedImprovement(x, gpModel)
[mu, sigma] = predict(gpModel, x);
y_best = min(gpModel.Y);
z = (y_best - mu) ./ sigma;
ei = (y_best - mu) .* normcdf(z) + sigma .* normpdf(z);
ei(sigma == 0) = 0;
end
% Modify the observation
function y_modified = modifyObservation(y)
y_modified = y + randn * 0.01; % Example modification
end
Hope this helps!
