【機器學習】資料預處理

一、回歸算法預處理FeatureNormalize

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;
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. 
              
for i=1:size(X,2),
  mu(i)=mean(X(:,i));
  sigma(i)=std(X(:,i));
  for j=1:size(X,1),
    X_norm(j,i)=(X(j,i)-mu(i))/sigma(i);
  end
end


end