From 2bf076c2fc059c55c836e9d66d3bde5d57df51b3 Mon Sep 17 00:00:00 2001
From: neingeist <neingeist@bl0rg.net>
Date: Thu, 2 Oct 2014 21:32:24 +0200
Subject: [PATCH] Gradient descent for one variable

---
 ex1/gradientDescent.m | 13 ++++---------
 1 file changed, 4 insertions(+), 9 deletions(-)

diff --git a/ex1/gradientDescent.m b/ex1/gradientDescent.m
index 3f66abf..89734f3 100644
--- a/ex1/gradientDescent.m
+++ b/ex1/gradientDescent.m
@@ -1,6 +1,6 @@
 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 
+%   theta = GRADIENTDESENT(X, y, theta, alpha, num_iters) updates theta by
 %   taking num_iters gradient steps with learning rate alpha
 
 % Initialize some useful values
@@ -11,21 +11,16 @@ for iter = 1:num_iters
 
     % ====================== YOUR CODE HERE ======================
     % Instructions: Perform a single gradient step on the parameter vector
-    %               theta. 
+    %               theta.
     %
     % Hint: While debugging, it can be useful to print out the values
     %       of the cost function (computeCost) and gradient here.
     %
-
-
-
-
-
-
+    theta = theta - (alpha/m) * X' * (X*theta - y);
 
     % ============================================================
 
-    % Save the cost J in every iteration    
+    % Save the cost J in every iteration
     J_history(iter) = computeCost(X, y, theta);
 
 end