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function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters)
%GRADIENTDESCENT Performs gradient descent to learn theta
%   theta = GRADIENTDESCENT(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 (computeCost) and gradient here.
    %
    %theta(iter)=theta(iter)-0.01*(1/m)*(((theta(1)+theta(2))*X-y)*X(iter,2))
    theta=theta-(alpha*(1/m)*(X*theta-y)*X(iter,2);
         % ============================================================
    % Save the cost J in every iteration    
    J_history(iter) = computeCost(X, y, theta);

end

end
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1 回答 1

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theta=theta-(alpha*(1/m)*(X*theta-y)*X(iter,2);

据我所知,括号不平衡?

你错过了一个右括号),不是吗?

于 2021-02-15T15:15:38.930 回答