Functional erf minimization with scalar theta

nn_example.m

environment = [4, 5, 7, 8];
expectation = 23;

% Initial weight
theta = 0.5;
theta_old = theta - 0.25;

while abs(theta - theta_old) > 1e-12
    % Derivative of squared error: ln = (expectation - reinforcement)^2
    dln_dr = -2 * (expectation - theta * environment);
    % By chain rule: r = theta * environment
    dr_dw = transpose(environment); % env is a column vector
    dln_dw = dln_dr * dr_dw;
    
    % Save old theta so one can check for difference
    theta_old = theta;
    
    % Substitute derivatives into theta update
    theta = theta - 0.001 * (dln_dw); % 1xn * nx1
end

I'm commenting so I can check it on my computer lol