Gradient Descent in Matlab
machine-learning, matlab
Solution
The error that you got `Error using .* Matrix dimensions must agree. Error in gradientDescent (line 20) temp1 = theta(2,1) - (alpha/m)*sum((X*theta-y).*X);` means that the `.*` is not working. So, before that line, add in the following code:
size(X*theta-y)
size(X)
If you want to do `(X*theta-y).*X`, then both `X*theta-y` and `X` should be the same size. If they aren't, you will need to check your algorithm.
Problem
I am taking machine learning class in courseera. The machine learning is a pretty area for me. In first programming exercise I am having some difficulties in gradient decent algorithm. If anyone can help me I will be appreciate. Here is the instructions for updating thetas; "You will implement gradient descent in the file gradientDescent.m. The loop structure has been written for you, and you only need to supply the updates to θ within each iteration. ``` function [theta, J_history] = gradientDescent(X, y, theta, alpha, num_iters) %GRADIENTDESCENT Performs gradient descent to learn theta % theta = GRADIENTDESENT(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. % % ============================================================ % Save the cost J in every iteration J_history(iter) = computeCost(X, y, theta); end end ``` So here is what I did to update thetas simultaneously; ``` temp0 = theta(1,1) - (alpha/m)*sum((X*theta-y)); temp1 = theta(2,1) - (alpha/m)*sum((X*theta-y).*X); theta(1,1) = temp0; theta(2,1) = temp1; ``` I am getting error when I run this code. Can anyone help me please?