From 1cc58802eb3a7518b0bd9186c680cf30ef2b82a6 Mon Sep 17 00:00:00 2001
From: neingeist <neingeist@bl0rg.net>
Date: Thu, 6 Nov 2014 12:04:53 +0100
Subject: [PATCH] Learning curve function

---
 ex5/learningCurve.m | 46 ++++++++++++++++++++++++++++++---------------
 1 file changed, 31 insertions(+), 15 deletions(-)

diff --git a/ex5/learningCurve.m b/ex5/learningCurve.m
index 6ea4333..f197248 100644
--- a/ex5/learningCurve.m
+++ b/ex5/learningCurve.m
@@ -1,11 +1,11 @@
 function [error_train, error_val] = ...
     learningCurve(X, y, Xval, yval, lambda)
-%LEARNINGCURVE Generates the train and cross validation set errors needed 
+%LEARNINGCURVE Generates the train and cross validation set errors needed
 %to plot a learning curve
 %   [error_train, error_val] = ...
 %       LEARNINGCURVE(X, y, Xval, yval, lambda) returns the train and
-%       cross validation set errors for a learning curve. In particular, 
-%       it returns two vectors of the same length - error_train and 
+%       cross validation set errors for a learning curve. In particular,
+%       it returns two vectors of the same length - error_train and
 %       error_val. Then, error_train(i) contains the training error for
 %       i examples (and similarly for error_val(i)).
 %
@@ -22,9 +22,9 @@ error_train = zeros(m, 1);
 error_val   = zeros(m, 1);
 
 % ====================== YOUR CODE HERE ======================
-% Instructions: Fill in this function to return training errors in 
-%               error_train and the cross validation errors in error_val. 
-%               i.e., error_train(i) and 
+% Instructions: Fill in this function to return training errors in
+%               error_train and the cross validation errors in error_val.
+%               i.e., error_train(i) and
 %               error_val(i) should give you the errors
 %               obtained after training on i examples.
 %
@@ -35,25 +35,41 @@ error_val   = zeros(m, 1);
 %       the _entire_ cross validation set (Xval and yval).
 %
 % Note: If you are using your cost function (linearRegCostFunction)
-%       to compute the training and cross validation error, you should 
-%       call the function with the lambda argument set to 0. 
+%       to compute the training and cross validation error, you should
+%       call the function with the lambda argument set to 0.
 %       Do note that you will still need to use lambda when running
 %       the training to obtain the theta parameters.
 %
 % Hint: You can loop over the examples with the following:
 %
-%       for i = 1:m
-%           % Compute train/cross validation errors using training examples 
-%           % X(1:i, :) and y(1:i), storing the result in 
-%           % error_train(i) and error_val(i)
-%           ....
-%           
-%       end
+for i = 1:m
+  % Compute train/cross validation errors using training examples
+  % X(1:i, :) and y(1:i), storing the result in
+  % error_train(i) and error_val(i)
+
+  X_ = X(1:i,:);
+  y_ = y(1:i);
+
+  % Train with regularization
+  lambda = 1;
+  theta = trainLinearReg(X_, y_, lambda);
+
+  % Compute the error with lambda = 0
+  lambda = 0;
+  error_train(i) = linearRegCostFunction(X_, y_, theta, lambda);
+  error_val(i) = linearRegCostFunction(Xval, yval, theta, lambda);
+
+end
 %
 
 % ---------------------- Sample Solution ----------------------
 
 
+%       for i = 1:m
+%           % Compute train/cross validation errors using training examples
+%           % X(1:i, :) and y(1:i), storing the result in
+%           % error_train(i) and error_val(i)
+%           ....