Clean up whitespace

ex1/featureNormalize.m

@@ -1,5 +1,5 @@
 function [X_norm, mu, sigma] = featureNormalize(X)
-%FEATURENORMALIZE Normalizes the features in X 
+%FEATURENORMALIZE Normalizes the features in X
 %   FEATURENORMALIZE(X) returns a normalized version of X where
 %   the mean value of each feature is 0 and the standard deviation
 %   is 1. This is often a good preprocessing step to do when
@@ -13,22 +13,18 @@ sigma = zeros(1, size(X, 2));
 % ====================== YOUR CODE HERE ======================
 % Instructions: First, for each feature dimension, compute the mean
 %               of the feature and subtract it from the dataset,
-%               storing the mean value in mu. Next, compute the 
+%               storing the mean value in mu. Next, compute the
 %               standard deviation of each feature and divide
 %               each feature by it's standard deviation, storing
-%               the standard deviation in sigma. 
+%               the standard deviation in sigma.
 %
-%               Note that X is a matrix where each column is a 
+%               Note that X is a matrix where each column is a
-%               feature and each row is an example. You need 
+%               feature and each row is an example. You need
-%               to perform the normalization separately for 
+%               to perform the normalization separately for
-%               each feature. 
+%               each feature.
 %
 % Hint: You might find the 'mean' and 'std' functions useful.
-%       
+%
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