merwan / ml-class Goto Github PK

for i=1:length(lambda_vec)
	lambda = lambda_vec(i);
	theta = trainLinearReg(X, y, lambda);
	error_train(i) = linearRegCostFunction(X, y, theta, lambda);
	error_val(i) = linearRegCostFunction(Xval, yval, theta, lambda);
end

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);
sum1=zeros(size(X,2),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.
    %

notsum=((X*theta)-y)
sum1=(alpha/m)*(sum(notsum.*X))
theta=theta-sum1'









    % ============================================================

    % Save the cost J in every iteration    
    J_history(iter) = computeCostMulti(X, y, theta);

end

end