# wangyif2/LogisticRegression

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 function d = check(f, X, e, P1, P2, P3, P4, P5); % checkgrad checks the derivatives in a function, by comparing them to finite % differences approximations. The partial derivatives and the approximation % are printed and the norm of the diffrence divided by the norm of the sum is % returned as an indication of accuracy. % % usage: checkgrad('f', X, e, P1, P2, ...) % % where X is the argument and e is the small perturbation used for the finite % differences. and the P1, P2, ... are optional additional parameters which % get passed to f. The function f should be of the type % % [fX, dfX] = f(X, P1, P2, ...) % % where fX is the function value and dfX is a vector of partial derivatives. % % Carl Edward Rasmussen, 2001-08-01. argstr = [f, '(X']; % assemble function call strings argstrd = [f, '(X+dx']; for i = 1:(nargin - 3) argstr = [argstr, ',P', int2str(i)]; argstrd = [argstrd, ',P', int2str(i)]; end argstr = [argstr, ')']; argstrd = [argstrd, ')']; [y dy] = eval(argstr); % get the partial derivatives dy dh = zeros(length(X),1) ; for j = 1:length(X) dx = zeros(length(X),1); dx(j) = dx(j) + e; % perturb a single dimension y2 = eval(argstrd); dx = -dx ; y1 = eval(argstrd); dh(j) = (y2 - y1)/(2*e); end disp([dy dh]) % print the two vectors d = norm(dh-dy)/norm(dh+dy); % return norm of diff divided by norm of sum
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