39 lines
990 B
Matlab
39 lines
990 B
Matlab
function [theta, J_history] = gradientDescentMulti(X, y, theta, alpha, num_iters)
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%GRADIENTDESCENTMULTI Performs gradient descent to learn theta
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% theta = GRADIENTDESCENTMULTI(x, y, theta, alpha, num_iters) updates theta by
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% taking num_iters gradient steps with learning rate alpha
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% Initialize some useful values
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m = length(y); % number of training examples
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J_history = zeros(num_iters, 1);
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for iter = 1:num_iters
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% ====================== YOUR CODE HERE ======================
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% Instructions: Perform a single gradient step on the parameter vector
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% theta.
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%
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% Hint: While debugging, it can be useful to print out the values
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% of the cost function (computeCostMulti) and gradient here.
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%
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hx = X*theta;
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theta -= (alpha*sum((hx-y).*X)/m)';
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% ============================================================
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% Save the cost J in every iteration
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J_history(iter) = computeCostMulti(X, y, theta);
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end
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end
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