From cfcd8b02b0b8bcf4150b937283ef26c2b786e430 Mon Sep 17 00:00:00 2001
From: julien Lengrand-Lambert <julien@lengrand.fr>
Date: Sat, 12 Dec 2015 11:15:26 +0100
Subject: [PATCH] Finishes Week 6

---
 machine-learning-ex5/ex5/ex5.m             |   4 ++--
 machine-learning-ex5/ex5/polyFeatures.m    |  10 ++++------
 machine-learning-ex5/ex5/token.mat         | Bin 262 -> 262 bytes
 machine-learning-ex5/ex5/validationCurve.m |  10 ++++++----
 4 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/machine-learning-ex5/ex5/ex5.m b/machine-learning-ex5/ex5/ex5.m
index 1df5a1c..be23ba0 100644
--- a/machine-learning-ex5/ex5/ex5.m
+++ b/machine-learning-ex5/ex5/ex5.m
@@ -123,7 +123,7 @@ for i = 1:m
 end
 
 fprintf('Program paused. Press enter to continue.\n');
-pause;
+% pause;
 
 %% =========== Part 6: Feature Mapping for Polynomial Regression =============
 %  One solution to this is to use polynomial regression. You should now
@@ -153,7 +153,7 @@ fprintf('Normalized Training Example 1:\n');
 fprintf('  %f  \n', X_poly(1, :));
 
 fprintf('\nProgram paused. Press enter to continue.\n');
-pause;
+% pause;
 
 
 
diff --git a/machine-learning-ex5/ex5/polyFeatures.m b/machine-learning-ex5/ex5/polyFeatures.m
index f496f48..2643a38 100644
--- a/machine-learning-ex5/ex5/polyFeatures.m
+++ b/machine-learning-ex5/ex5/polyFeatures.m
@@ -15,11 +15,9 @@ X_poly = zeros(numel(X), p);
 %
 % 
 
-
-
-
-
+for i = 1:p
+   X_poly(:, i) = X.^i;
+end
 
 % =========================================================================
-
-end
+end
\ No newline at end of file
diff --git a/machine-learning-ex5/ex5/token.mat b/machine-learning-ex5/ex5/token.mat
index 452c919109d09ab02d3d354d31b4926d2c0b3256..f036d5d5c68ba028b2610f0421707c7807a575cf 100644
GIT binary patch
delta 28
jcmZo;YGaz<z+-4-Xli9(p<rZSXgV=ad13<V#F9w>X7C5v

delta 28
jcmZo;YGaz<z++%#WNKw%tYBndXgV=ad13<V#F9w>X5$Ch

diff --git a/machine-learning-ex5/ex5/validationCurve.m b/machine-learning-ex5/ex5/validationCurve.m
index 24b56bc..14f184e 100644
--- a/machine-learning-ex5/ex5/validationCurve.m
+++ b/machine-learning-ex5/ex5/validationCurve.m
@@ -41,10 +41,12 @@ error_val = zeros(length(lambda_vec), 1);
 
 
 
-
-
-
-
+for i = 1:length(lambda_vec)
+    lambda = lambda_vec(i);
+    [theta] = trainLinearReg(X, y, lambda);
+    error_train(i) = linearRegCostFunction(X, y, theta, 0);  
+    error_val(i) = linearRegCostFunction(Xval, yval, theta, 0);  
+end