【机器学习】数据预处理
2017-01-24 00:06
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一、回归算法预处理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
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