So i am trying to implement/solve the first programming excersise from Andrew ng`s machine learn cours on coursera. I have trouble implementing linear gradient descent(for one variable) in octave. I don't get the same paramters values back like in the solution but my parameters goes in the same direction(at least i think so). So i may have somewhere in my code a bug. Maybe someone who has more experience than me can enlighten me.
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); theta1 = theta(1); theta2 = theta(2); temp0 = 0; temp1 = 0; h = X * theta; 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. % temp0 = 0; temp1 = 0; for i=1:m error = (h(i) - y(i)); temp0 = temp0 + error * X(i, 1));; temp1 = temp1 + error * X(i, 2)); end theta1 = theta1 - ((alpha/m) * temp0); theta2 = theta2 - ((alpha/m) * temp1); theta = [theta1;theta2]; % ============================================================ % Save the cost J in every iteration J_history(iter) = computeCost(X, y, theta); end end
My exspected results for excersise 1 with theta initialized with [0;0] should be for theta1: -3.6303 and for theta2: 1.1664
But i become as output theta1 is 0.095420 and thetha2 is 0.51890
This is the formula i use for linear gradient descent.
EDIT1: Edited code. Now i got for theta1:
and for theta2