title: linear regression with feature normalization matlab code
body: <p>I did it two ways, why is the first way(starting on line with mu=mean(X) not working?  what's the difference?  </p>
  
  <pre><code> function [X_norm, mu, sigma] = featureNormalize(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
      %   working with learning algorithms.
  
      % You need to set these values correctly
      X_norm = X;
      mu = zeros(1, size(X, 2));
      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 
      %               standard deviation of each feature and divide
      %               each feature by it's standard deviation, storing
      %               the standard deviation in sigma. 
      %
      %               Note that X is a matrix where each column is a 
      %               feature and each row is an example. You need 
      %               to perform the normalization separately for 
      %               each feature. 
      %
      % Hint: You might find the 'mean' and 'std' functions useful.
      %       
  
      %mu=mean(X)
      %X_norm=X-mu;
      %sigma=std(X_norm)
      %X_norm(1)=X_norm(1)/sigma(1)
      %X_norm(2)=X_norm(2)/sigma(2)
  
  
  
      % Calculates mean and std dev for each feature
      for i=1:size(mu,2)
          mu(1,i) = mean(X(:,i)); 
          sigma(1,i) = std(X(:,i));
          X_norm(:,i) = (X(:,i)-mu(1,i))/sigma(1,i);
      end
  
  
  
  
  
  
  
      % ============================================================
  
      end
  </code></pre>