jens
7b34529b24
git-svn-id: http://moon:8086/svn/matlab/trunk@91 801c6759-fa7c-4059-a304-17956f83a07c
42 lines
1.2 KiB
Matlab
42 lines
1.2 KiB
Matlab
function numgrad = computeNumericalGradient(J, theta)
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% numgrad = computeNumericalGradient(J, theta)
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% theta: a vector of parameters
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% J: a function that outputs a real-number. Calling y = J(theta) will return the
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% function value at theta.
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% Initialize numgrad with zeros
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numgrad = zeros(size(theta));
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%% ---------- YOUR CODE HERE --------------------------------------
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% Instructions:
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% Implement numerical gradient checking, and return the result in numgrad.
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% (See Section 2.3 of the lecture notes.)
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% You should write code so that numgrad(i) is (the numerical approximation to) the
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% partial derivative of J with respect to the i-th input argument, evaluated at theta.
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% I.e., numgrad(i) should be the (approximately) the partial derivative of J with
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% respect to theta(i).
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%
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% Hint: You will probably want to compute the elements of numgrad one at a time.
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epsilon = 1e-4;
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for i =1:length(numgrad)
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oldT = theta(i);
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theta(i)=oldT+epsilon;
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pos = J(theta);
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theta(i)=oldT-epsilon;
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neg = J(theta);
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numgrad(i) = (pos-neg)/(2*epsilon);
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theta(i)=oldT;
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if mod(i,100)==0
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fprintf('Done with %d\n',i);
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end;
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end;
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%% ---------------------------------------------------------------
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end
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