Advertisement
Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
- %GRADIENTDESCENTMULTI Performs gradient descent to learn theta
- % theta = GRADIENTDESCENTMULTI(x, y, theta, alpha, num_iters) updates theta by
- % taking num_iters gradient steps with learning rate alpha
- % Initialize some useful values
- m = length(y); % number of training examples
- J_history = zeros(num_iters, 1);
- for iter = 1:num_iters
- % ====================== YOUR CODE HERE ======================
- % Instructions: Perform a single gradient step on the parameter vector
- % theta.
- %
- % Hint: While debugging, it can be useful to print out the values
- % of the cost function (computeCostMulti) and gradient here.
- %
- theta_old = theta;
- hipoteza = X * theta_old;
- diff = hipoteza - y;
- for j = 1:length(theta)
- predvsota(j) = diff(j) .* X(j);
- vsota = sum(predvsota);
- theta(j) = theta_old(j) - (alpha * (1 / m)) * vsota;
- end;
- % ============================================================
- % Save the cost J in every iteration
- J_history(iter) = computeCostMulti(X, y, theta);
- end
- end
Advertisement
Add Comment
Please, Sign In to add comment
Advertisement