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neingeist 2014-10-02 22:20:31 +02:00
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ex1/featureNormalize.m
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@ -1,5 +1,5 @@
function [X_norm, mu, sigma] = featureNormalize(X) 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 % FEATURENORMALIZE(X) returns a normalized version of X where
% the mean value of each feature is 0 and the standard deviation % the mean value of each feature is 0 and the standard deviation
% is 1. This is often a good preprocessing step to do when % 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 ====================== % ====================== YOUR CODE HERE ======================
% Instructions: First, for each feature dimension, compute the mean % Instructions: First, for each feature dimension, compute the mean
% of the feature and subtract it from the dataset, % 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 % standard deviation of each feature and divide
% each feature by it's standard deviation, storing % 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. % Hint: You might find the 'mean' and 'std' functions useful.
% %