featureNormalize.m

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.

X_norm = X;

% size(X, 2) = # of columns in matrix X
% mu and sigma = row vector of size 1xn where n is the # of 
% rows in X (# of training data)
mu = zeros(1, size(X, 2));
sigma = zeros(1, size(X, 2));

% 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. 

% Parameter X: Any real mxn matrix 
%      Return: mu = 1xn row vector
%              sigma = 1xn row vector sigma
%              X_norm = mxn matrix which has been normalized 
%                       with mu and sigma
numberOfColumnsInX_norm = columns(X_norm);
% works with input matrix X of any size
for i = 1:numberOfColumnsInX_norm, 
	meanOfCurrentFeatureInX = mean(X(:, i));
	mu(:, i) = meanOfCurrentFeatureInX;

	X_norm(:, i) = X_norm(:, i) .- mu(:, i);

	standardDeviationOfCurrentFeatureInX = std(X(:, i));
	sigma(:, i) = standardDeviationOfCurrentFeatureInX;

	X_norm(:, i) = X_norm(:, i) ./ sigma(:, i);
end

end